ORI GI NALARTI CLESocialnetworksandonlineenvironments:whenscienceandpracticeco-evolveDevanRosenGeorgeA.BarnettJangHyunKimReceived:14July2010 / Accepted:2August2010 / Publishedonline:5October2010TheAuthor(s)2010.ThisarticleispublishedwithopenaccessatSpringerlink.comAbstract The science of social network analysis hasco-evolvedwiththedevelopment of onlineenvironmentsandcomputer-mediatedcommunication. Uniqueandpre-cise data available from computer and information systemshave allowed network scientists to explore novel socialphenomena and develop new methods. Additionally,advances in the structural analysis and visualization ofcomputer-mediatedsocial networkshaveinformeddevel-opers andshapedthedesignof social media tools. Thisarticlereviews someexamples of researchthat highlightthe ways that social network analysis has evolved withonlinedata. Examplesincludetheinternational hyperlinknetwork, politicalblogsandhyperlinks, socialmedia, andmulti-user virtual environments. Thedataavailablefromonline environments makes several important contributionstonetworkscience, includingreliablenetworkowdata,unique forms of relational data across a myriad of contexts,anddynamicdataallowingfor longitudinal analysis andtheanimationofsocialnetworks.Keywords Socialnetworkanalysis Computer-mediatedcommunication Informationsystems Hyperlinknetworks Socialmedia Socialnetworking1 IntroductionA social network is generally dened as a system with a setof social actors anda collectionof social relations thatspecify howthese actors are relationally tied together(WassermanandFaust 1994). Networkanalysisprovidestwo purposes, revealing the underlying social structuresand discovering the dynamic interactions among socialactors. Networkanalysis identies thesystems structurethroughexaminingtherelations amongthesystemcom-ponents,itsactors(RogersandKincaid1981).Computer and information systems are electroniccommunicationnetworksthat arestructuredinorder thatdata, information, andmessagesmaybepassedfromonelocation in the network to another over multiple links:transmission lines (copper wire, coaxial cable, opticalber, and wireless connections including satellites) andthroughvariousnodes(generallycomputers). Whenthesenetworkslinkpeople(orhigherlevel social systemssuchas work groups, organizations, or nations) as well asmachines, theybecomesocialnetworksormorepreciselycomputer-mediated social infrastructures. Examples oftelecommunication networks include the Internet: theglobal networkofnetworks(Barnett et al. 2001b; Barnettand Park 2005; Park et al. 2010), public switchedtelephone networks (POTS/PSTN) (Barnett 1999, 2001;BarnettandSalisbury1996)andtheglobalTelexnetwork(AhnandBarnett 1995), aswell asnumerousproprietarycomputernetworksforthecommunicationofbusinessandnancial information(e.g., theATMnetwork) (SalisburyandBarnett 1999).Thegoal ofthisarticleistoreviewsomeexamplesofresearch on socialnetworksin onlineenvironments. It willbeginbyexaminingtheinternational hyperlinknetwork.The next section will focus specically on blogs andD. Rosen J. H.KimDepartmentofSpeech, UniversityofHawaii,326GeorgeHall,2560CampusRd.,Honolulu,HI96822,USAe-mail:rosend@hawaii.eduJ. H.Kime-mail:jangkim@hawaii.eduG. A.Barnett(&)DepartmentofCommunication, UniversityofCalifornia,Davis, OneShieldsDrive, Davis,CA95616, USAe-mail:gbarnett@ucdavis.edu1 3SOCNET(2011)1:2742DOI10.1007/s13278-010-0011-7political hyperlinks. Athirdsectionwill examine socialmedia such as social networking sites (SNS), followed by asection on networks in multi-user virtual environments(MUVEs) (e.g., Active Worlds, Second Life). The nalsection will discuss the future of online social networks andcontributionstothescienceofnetworks, andimplicationsforfutureresearch.2 TheinternationalhyperlinknetworkFewstudies have examined the international Internetsstructure. One reason for this is that the Internet is a packet-switchednetworkunlike the telephone, whichdevotes asingle circuit toeachindividual message. Consequently,theoriginanddestinationof individual messages cannotbe determined (Barnett and Park 2005). An alternativeapproach that allows the examination of internationalInternet trafcis theanalysis of inter-domainhyperlinks(Barnett et al. 2001a, b). Ahyperlinkisthetechnologicalcapabilitythat enablesaWebsitetolinkseamlesslywithanother, generallythroughaclickofamouse(Parket al.2004). The WorldWide Webmaybe denedas a dis-tributed hypertext system consisting of a virtual network ofcontentandhyperlinks, withbillionsofinter-linkedpages(Almindt andIngwersen1997; KleinbergandLawrence2001). TheWebhasnoengineeredarchitecture, andassuch it is a self-organized systemwith a well-denedstructure of linkage that implies an underlying socialstructure (Chakrabarti et al. 1999; Shumate and Lipp2008). This sectionexamines theWebs emergent socialstructureasatechnological linkandcommunicationnet-workatthelevelofnationstates.In the rst large-scale study of the international Internet,Barnett et al. (2001b) examined data on the bilateralInternet links amongnations obtainedfromOrganizationfor Economic Co-operation and Development (OECD).Thenumberofinter-domainhypertext linksembeddedinWeb sites between all TLDs (top-level domains, such as .caforCanada)of29OECDmembercountriesand6gTLDs(generictop-leveldomains, .com, .net, .int, .gov, .eduand.org) were gatheredfor July1998(OECD1998). Thesecountries represented approximately 96% of Internet trafcfor July1998. However, missingfromtheanalysis werenon-OECDmembers including such signicant Internetusers as Brazil, Israel, India, Singapore, and China.Becausenoone TLDrepresentedInternet trafc for theUSA, .edu, .us, and.govwerecombinedtodesignatetheUSA.TheothergTLDs,.com,.org,.int,and.netwerenotincludedinthisgroupingbecauseaccesstothesegTLDswasnotexclusivelyAmerican.The result indicated that .comwas the most centralnode, followed by .net. Also, the USA was the most centralcountry, thenucleusof worldwideWebtrafc. SincetheInternetwasdevelopedintheUSAandbecauseofitslowtelecommunication costs for high-speed bandwidth, itbecamethetrafchub.Atthattime,itaccountedfor58%of all Internet host, andonly6of thetop100Websiteswerebasedoutsidethecountry(Cukier 1999). ThenextmostcentralnationsweretheUK, Canada, Germany, andAustralia. Most peripheral in the network were Iceland andTurkey. ThecorrelationbetweencentralityandGDPwas0.974 (p \0.000), indicating that a nations position in thenetwork was a function of its total wealth. Aclusteranalysis revealed that the OCED nations and gTLDsformedasinglegroupcenteredaboutthe.com.netdyad.Therewerenosub-groupingsduetogeography,language,orculture.The results further revealed that the structure of the Webwasrelatedtoanumber of exogenousvariablesandpre-existing networks, including the international telephonenetwork(r=0.628, p \0.000), air trafc network(r=0.730, p \0.000), tradenetwork(r=0.595, p \0.000),international science citation network (r= 0.486, p\0.000),internationalstudentows(r=0.356, p \0.000),language(r=0.202,p \0.002),andasynchrony, denedasthedifferenceintimezonesbetweennations capitals(r=0.113, p=0.115). Physical distance, however, wasnot related to the structure of international hyperlinks(r=0.012, p=0.416). The cost of communicating viatheInternet was unrelatedtodistance(r=-0.008, p=0.388). Thecombinedeffectsoftheantecedentsindicatedthat between 62 and 64% of the variance in the structure ofhyperlinkowscouldbeaccountedforbytransportation,telecommunications, science and asynchrony, and eithertradeorstudent ows, withtransportationbeingthemostsignicant determinant. These results led the authors toconclude that Internet represents an autopoietic system(Maturana and Varela 1980; Barnett 2005), evolvingthrough self-replication of the telecommunications net-work, but withgrowthtoaccommodatefor thephysicaldisplacement oftheinteractantsandtheabilitytorapidlyexchangeandstorevast amountsofinformationbyotherthanvoice(Barnettetal. 2001a).Alongsimilarlines, Halavais(2000)examinedtheroleof geographicborders of thehyperlinkpatterns of 4,000Web sites. He found that Web sites were most likely to linktoanother siteinthesamecountry. Whentheydidlinkacrossnational borders, most oftenit wastohostsintheUSA. Brunn and Dodge (2001) analyzed inter-domainhyperlinks among 174 geographic TLDs. They treated Websites incomingandoutgoinglinks separatelyanddevel-opeddescriptivestatisticsandcross-tabulationanalysisbycountryandregion. Ciolek(2001)examinedthedirectionandvolumeofhyperlinksamongtenEastAsiancountriesand found that while Japan had the greatest volume of28 D. Rosenetal.1 3hyperlinks, 92% were directed to other Japanese Web sites.Singapore imported 27%of its links and China 25%.Indonesia attracted 30%of all pages with internationallinksfromtheothercountries. Bharat et al. (2001)foundthat there was a much higher number of intra-nationallinks thanties toother countries. Typically, only1%oflinks were to Web sites in another country. When the linksamong the most central countries were removed, geo-graphical, linguistic, and political factors impacted thestructureoftheWeb.Barnett and Park (2005) expanded on earlier research bygathering data on the number of bilateral inter-domainhyperlinksamongnationsusingAltaVista. Includedwerethe TLDs of 47 nations including all OECDmembercountries(exceptPoland)and6gTLDs.Notableadditionsto the earlier research included Brazil, India, China, Russia,SouthAfrica,Israel,Singapore,andIndonesia.Thesedatawerecollectedon30January2003. Together, theseTLDsrepresent approximately98%of Internet trafc (InternetSoftware Consortium 2001). Again, because no single TLDtotallyrepresents theUSA, .edu, .mil, .us and.govwerecombinedtodesignatetheUSA(.usa).The results indicated that the hyperlink network in 2003wascompletelyinterconnected. Asin1998, theUSAwasthe most central country, followed by Australia, UK,China, Japan, Canada, andGermany. Most peripheral inthenetworkwereUruguay, Luxemburg, UAE, Thailand,Slovakia, andRomania. Whenthe directionof linkwasconsidered, the USAwas the most central inin-degree,followedbyIndonesia, India, Italy, andFrance. Onthisindicator, Uruguay, UAEand Czech Republic were themost peripheral. Germany was the most central in out-degree, followedbytheUK, USA, andAustralia. Indone-sia, UAE, andIndiawerethemost peripheral. Aclusteranalysisofthehyperlinknetworkrevealedasinglegroupcentered about the .usa.au dyad, the two most centralnodes.Barnett and Park (2005) also analyzed data on thebilateral bandwidth capacity obtained fromTeleGeogra-phy(2003). Bandwidthdetermineshowphysical networkcomponentstransport packetsof datafrompoint topointas opposedtotheTCP/IPfor whichgeographyis irrele-vant (Townsend2001). Theseconnectionsarenon-direc-tional. Thedensityof thebandwidthnetworkfor the47countries that compose the hyperlink network indicatedthat 18.5%of the possible direct links are present forthese countries. The USAwas by far the most centralcountry in terms of bandwidth, followed by the UK,Germany, Hong Kong, Singapore, Japan, and France.Most peripheral were Iceland, Lithuania, Morocco,Croatia, andGuatemala.Aclusteranalysisresultedinthreemajorgroupings:(1)the English speaking countries (USA, UK, Canada,Australia, andNewZealand)withnorthernEurope(Scan-dinavia, BelgiumandtheNetherlands) andeasternAsia,(2) Latin America, and (3) Franco-German Europe (France,Germany, Austria, Italy, Spain, Switzerland, and the CzechRepublic).Thenetworkresembledawheel,withtheUSAat the hub with spokes to the individual countries andclusters of nations. TheUSA dominated Internet ows dueto itspositionin the network. While therewere some linksentirely within Europe or the Asian-Pacic region andlimitedlinks withinLatinAmerica, links betweentheselocalitiesprimarilywent throughtheUSA. Further, eventhe connections within specic regions may have beenroutedthroughtheUSAbecauseoflimitedwithin-regionbandwidth. Clearly, theUSAwasinpositiontoact asaninformation broker or gatekeeper in the internationalInternet.Townsends(2001, p. 1701) examinationof the Internetbandwidthresultedinasimilarconclusion,every region and nearly every country has a directInternet connectiontotheUnitedStates, direct con-nections betweenother countries are less common.Furthermore, direct connections between differentmajor regions such as Asia and Europe are practicallynonexistentThis structure dictates that the U.S.Internet infrastructure functions as a massiveswitchingstationfor trafc that originates andter-minatesinforeigncountries.Barnett and Park (2005) correlated the hyperlink and thebandwidthnetworks. It was0.412(p=0.000). Addition-ally, there was a strong relationship (r=0.847, p=0.000)between both networks centralities, suggesting that theconnectivity pattern between hyperlinks and bandwidthweresimilar, indicatingthat thephysical infrastructureofthe Internet is an important determinant of which countriescommunicateviathismedium.Parket al. (2010) examinedthestructureof theinter-national hyperlinknetworkin2009andhowit changedfrom 2003. Data were collected in May 2009 using Yahoo.Yahooacquiredthe AltaVistain2004andhas kept thedatabaseforitssearchservice. Thus, thesearchalgorithmis the same as for 2003. According to http://www.worldwidewebsize.com/, 2009, Yahoo indexed about 47billionWebsitesat that time. Theactual datacollectionexaminedover 9.3billionhyperlinks among33.8billionsitesfrom273TLDs. Again, threeTLDsreservedfortheexclusive use of American institutions, .edu, .gov, and .milwere combined with .us to forma node for the USA.Because.com,.org,and.netarenotexclusivetotheUSA,theywerenot included. Thismayhaveresultedinabiasdescription of the network by underestimating the cen-tralityoftheUSAandothercountriesthatrelyheavilyontop-leveldomains.Onlinenetworks 291 3The 2009 international hyperlink network was com-pletelyinterconnected.TheUSAhadthelargestin-degreecentrality, followedbyGermany, UK, France, Japan, andSpain. Germany, UK, Japan, France, andSpain, not theUSA, have the highest out-degree centralities. Figure1shows the positions of the countries andtheir links, theconnection density among the nodes, and the relativestrengths of the hyperlink connections among the countries.Theseresultsindicatethatinthe2009hyperlinknetwork,theG7andseveral EUcountriesarecentral. Also, Braziland Russia have emerged as core countries integratingmoreperipheral nations. Brazil links SouthAmericaandRussia, theformer Soviet Republics. Additionally, basedon the cluster analysis, it appears that for the rst time thereare regional, cultural, and linguistic groupings; a LatinAmerican group, cliques, centered about Russia and China,a Scandinavian group, as well as a core group composed oftheG7countries.Toinvestigatethechangingglobal networkgeneratedby the World Wide Web, Park et al. (2010) comparedthehyperlinkrelations among47countries in2009withthe same set from2003. The results for the hyperlinknetwork in 2009 are similar to those reported for 2003(Barnett and Park 2005). The USA is still the mostcentral countryalongwithGermany, UK, France, Japan,and Spain. The semi-peripheral countries include Neth-erlands, Austria, Switzerland, Belgium, Australia, Brazil,Mexico, China, India, andRussia. UAE, Israel, Estonia,Uruguay, and Luxembourg are the most peripheral.Variousmeasuresofcentralitybetweenthetwopointsintime provide further evidence for the stability in thenetwork over time, averaging about 0.80 depending onthemeasure.However, theoverallcorrelationbetweenthe2009and2003 networks is only 0.406 (p \0.01) accounting for onlyabout 16%of thevarianceinthe2009networkby2003.Thereweresomeobvious andinterestingchanges. First,the international hyperlinknetworkbecame more highlycentralized. The greatest departures fromthe predictedchangeswerefor themost central countries. Europeasawhole, especiallyGermany, became muchmore central.UK, France, Spain, Italy, andJapansout-degreecentrali-ties grew more than expected. USA, Germany, UK, France,Japan, and Spains in-degree grewmore than expected.Second, the BRIC (Brazil, Russia, India, and China)countriesshowedvariouschanges. Brazil grewmorethanpredicted, Russiaas predicted, andChinahas fewer out-wardlinksthanexpected. Perhaps, thisisduetointernaldomesticgrowthor theuseof theChineselanguagelim-itingits contacts withWesternEurope. India hadfewerinwardlinksthanexpected. Third, thecentralitiesaredis-tributed as a power curve (Barabasi 2002), suggestingdisproportionalgrowthinthenumberofhyperlinksbythemore central countries and support for the notion ofFig.1 International hyperlink ow network. The size of theconcentric circles shows the hyperlink connection density amongcountries. The thickness of the line connecting two nodes isproportional totheconnectiondensitybetweenthetwonodes. Onlyties with [1,000,000hyperlinks areshown. All isolates havebeenremovedfromthegure.IndividualTLDswith gray circlesnotonlyrepresentcountries, butalsogenericTLDs.Forexample,.TVstandsfortheislandnationofTuvaluandfortelevision30 D. Rosenetal.1 3preferential attachment. Fourth, whiletherewasonlyonegroupin2003, regional, cultural, andlinguisticgroupingsformed in Latin America and Scandinavia, and aroundChinaandRussia,suggestingthathybridization,increasedcentralization toward core-peripheral countries, andincreasing autonomous diversication of semi-peripheralcountriestookplace.There was increased concentration in the networkbetween2003and2009.Ithasbecomecentralizedaroundseveralhubs. The Gini-coefcient for 2009network showsthat international hyperlinknetworkis centralizedaboutseveral countriesthat act asthehub(thecoregroups: theG7?Spain). ThecompositeGini-scoreof 2009networkwas0.466, whileitwasonly0.291in2003.Animportant issuethat remainsunresolvedininterna-tional hyperlinkresearchis howimperfect spatial infor-mationinadvertentlyalters theperceivedstructureof thenetwork(GrubesicandMurray2005). Traditionally, thisresearchhasnotincludedgTLDs.AsBarnettetal.(2010)point out, thereisaninherent biasintheanalysisof theinternational hyperlink network because it does not includegTLDs links in the examination of the links amongnational TLDs. That is, it does not account for thegeo-graphiclocationsof.com. Asaresult, theconnectivityoftheUSAandothernationstatesthatrelyheavilyon.comratherthannational TLDsareunderreported. Thereasonsfor not including the gTLDs are probably due to the ease ofdata mining the relations among ccTLDs, and the difcultyindetermininginwhichcountriestheseWebsitesresideandwhousesthesesites.Basedontheassumptionthat decomposing.comleadstoamoreaccuratedescriptionoftheinternational hyper-linknetwork, Barnett et al. (2010) investigatedadjustingthehyperlinknetworkusingdatafromAlexa.comonthepercentage of international Internet users for the mostfrequently visited .comWeb sites. They developed amethodtodecompose the three gTLDs (.com, .org, and.net) into the countries in which their servers or users resideand distributed the links proportionally to the nationalnodes. They applied the procedures and compared theresults obtained with the traditional methods. This wasaccomplishedasfollows.Alexa.com(http://www.alexa.com/topsites, 2009) liststhe 500 top Web sites based on the number of average dailyvisitors and the number of page views. For each listed site,Alexa.comprovidesthepercentageofglobalInternetuserswhovisitedthesitethepreviousday,theaverageoverthelast 7days, and 1 and 3months. Also, it provides thepercentageofusersfromallcountriesrepresenting [0.5%of the sites trafc. In September 2009, Google.com rankedrst withanaverageof34%oftheworldsInternetusersvisitingthesitedailyovertheprevious3months.Further,37.2% of its visitors were from the USA, 9.3% from India,3.7% from Brazil, and so on. Google.comwas followed byFacebook.com(22.6%)andYahoo.com(25.8%).Toestimatethelinkstrengthbetweentwonations on.com, thepercentageofInternetuserswasrst multipliedbythepercentageforeachcountryandthensummedforasample of .com sites. Since the percentage of Internet usersvisiting the various sites is distributed according to thepower law, only the most frequently visited sites weresampled. All .com sites with[0.5% of the worlds Internettrafc during the week of 1926 September 2009 wereexamined(N=110).Basedonthisanalysis,anestimated26.5%(sumofeachsiteusersfortheUSAis0.694outof2.337) of .com trafc involves visitors from the USA, 9.3%fromChina, 5.8%fromBrazil, 4.9%fromJapan, 3.3%fromUK, and3.1%fromGermany. Third, thesenumbersweremultipliedbythenumberofhyperlinkstoandfrom.com,whichinthisdatasetexceeded2.1billionincomingand4.0billionoutgoinghyperlinks. After the other USdomainnames(.edu, .gov, .mil, and.us) weresubtractedfromthe total number of incoming links it exceeded 2billion. Thus, the estimated number of incoming hyperlinksfromWebsite, otherthan.comfortheUSAwasover1.5billion(2billiontimes0.694). ForIndia,thisnumberwas327million, andBrazil almost 16million. Finally, thesenumbers were addedtothe reportedtotals basedexclu-sivelyontheir countrys domainname. This adjustmentaddressed the systematic bias in international hyperlinkanalysis.TheseproceduresaresummarizedinTable1.Barnett et al. (2010) examinedtheeffectsof adjustingthe international hyperlinkstructure byaddingthe linksfromthedecomposed.com, bycomparingthehyperlinkrelationsamong87countriesexcludingthe.comdatawiththoseincludingthe.comlinks. Boththeoriginal andtheadjusted networks are displayed in Fig. 2a and b. Theadjustedhyperlinknetworkshowedsignicant changesinthecentralityofseveralcountries,whichmakegreateruseof.com. TheUSsout-degreecentralityincreasedanditscentrality changed more than any other country whencomparedtothehyperlinknetworkexcluding.com. Also,China, Japan, and Indias centrality notably increased. Thisis probably due to the strong economic relationshipsbetweentheUSAandtheseother countries andChinaslargeportionof .comonspecicChineselanguageWebsites, suchasbaidu.com, qq.comandtaobao.com. Onthecontrary, the centrality of countries that do not heavily relyon.com, suchasEuropeancountries, decreased.Correlationsbetweenthetwosetsofcentralitiesscoresshowedthatthe additionof .com Websites did not changetherelativenetworkcentralitiesagreat deal. Thecorrela-tionsrangedfrom0.90to0.93dependingonthemeasure.The cell-wise correlation indicated that there were sys-tematicdifferencesbetweenthetwonetworks(r=0.755,p=0.00). Thetop20residuals involvedtheUSA(13),Onlinenetworks 311 3China(5), Japan(4), theUK(2), France(2), Korea(2),Germany(1), Spain(1), Canada(1), andIndia(1).AlthoughthisresearchmorepreciselydenedcountriesasnodesontheInternetthroughdecomposing.combasedon where their servers or users reside, there are still severalmethodological issuesthat must beaddressed. First, theseadjustments were not basedonthe volume of hyperlinkconnections. They were based on the proportion of Internetusers that used certain Web sites andusers country ofresidence. Thehyperlinkstoandfrom.comweredistrib-uted to various countries based on their residents Web siteuse. It wasassumedthat thisisanaccurateproxyforthedistribution of hyperlink connections for the countries. Thismight not bethecase. Second, noindicators of thereli-abilityof themeasurement procedures or thevalidityofYahoos searchenginewereprovided. Third, thereweredifculties in addressing nodes that share their domainnameswithvariousUSstates(e.g.,Canada,Germany,andIndonesia)(BarnettandPark2005).Finally,theemployedresearchprocedurecanberenedmorepreciselytodeneindividual countries as nodes on the Internet if othergTLDssuchas.net, .org, or.euWebsitescanbedecom-posedcorrectly. Thisresearchonlycracked.com.3 SocialnetworkanalysesamongpoliticalblogsSocial networkanalysis has beenusedtoinvestigatethethematicandrelational aspects of blogs [or weblogs]. Ablog is a web page that features personal journals orfocuses on the outside world including such topics ascurrent events(Blood2000). Blogscanbeclassiedintothose with general and specic interest. The former mainlydeals with personal thoughts, experiences, and usefulinformation that the bloggers want to share with theirvisitors. Thelatter focuses onaparticular topic, suchasculinary, art, politics, international relations, economics,music, popularculture, orliterature. Halavais(2004)stip-ulatesthat todaymanypeopleengageinbothabstractingother web pages and generating original content fortheir blogs. Blogs have become an integral part of thedynamicweb.Blogs includetwoaspects of onlineinteractions, con-tents, and relationships. They consist of varied contents andlinking to other web pages through hyperlinks. Socialnetworkanalysishasbeenmainlyemployedtounderstandthe linking practices and their structure (Park and Jan-kowski2008)ina certainthemesuchaspolitics, ahostingplatform(LiveJournal, Herringetal. 2007;Wallop, Lentoet al. 2006), or a certain nation (Korea, Park and Jankowski2008).Blog hyperlinks involve diverse agencies in politicalcommunicationincludingpolitical party, activist groups,andindividuals.Paststudiesfoundthatthesocialnetworkrevealed by hyperlink connections represented relation-shipsamongthoseagenciesandtheirrolesinthenetwork(ParkandJankowski2008;Norris2001;Parket al. 2004,2005;ParkandThelwall2003;Thelwall 2004;Kimetal.2010).Thissectionlimitsitsfocalpointtosocialnetworkanalysis of political blogs, because analyses of blogs with atheme ratherthan those of general interest efciently showtheefcacyofsocialnetworkanalysisinblogresearch.3.1 ThenatureofpoliticalblogsandbloggersPolitical bloggers canlter information, proactivelyseekbetterinformation,graspdiverseviews,evaluateopinions,andparticipate indiscussions (Blood2002, 2003). Webfeeds(RSS)aregoodexamplesofinteractivitycommonlyusedbyblogs.Onecansubscribetoablogbycuttingandpasting its RSS address to his/her own blog or blog-reader.Once any part of a blog is updated, the reader will beinstantly informed and he/she may reply or comment on theTable1 Proceduresforadjustinginternationalhyperlinkstrengthwith.com1. Multiplydailypercentageofuserstimespercentageforeachcountryanddivideby1002. RepeatforallselectedWebsites3. SumforallWebsitestodetermineeachcountystotal4. Sumallcountriestotals. Itis [100(becausepeopleusemorethanoneofthesesitesdailyonanaverage)5. Divideeachcountrystotalbythetotalforallcountries. Thisisthepercentageof.comforeachcountry6. Multiplythispercentageforeachcountrytimestherowandcolumnfor.com. Thisistheestimateof.comhyperlinkuseforeachcountry7. Addthisvaluetoeachcountryshyperlinknetworktietoeachothercountry(andfromeachothercountry).ThisistheadjustedhyperlinkdatasetFig.2 a International hyperlink structure excluding .com. The size ofthe concentric circles indicates the hyperlink connection densityamongcountries. Thethicknessofthelineconnectingtwonodesisproportional totheconnectiondensitybetweenthetwonodes. Onlythose ties exhibiting [500,000 hyperlinks are shown. N=87.bInternational hyperlinkstructureincluding.com. Thesizeof theconcentriccirclesindicatesthehyperlinkconnectiondensityamongcountries. The thickness of the line connecting two nodes isproportional totheconnectiondensitybetweenthetwonodes. Onlythosetiesexhibiting [1,500,000hyperlinks(3timesmorecomparedto hyperlink network excluding .com, based on 3 times degreedifference)areshown.N=87c32 D. Rosenetal.1 3Onlinenetworks 331 3posting (Halavais 2009). The power of blogs lies intransforming both the writers and readers from audienceto public and fromconsumer to creator (Blood2000). Political blogs offer a reciprocal relationship amongtheirusers(Halavais2004).Kaye (2005) surveyed 3,747 blog readers and found thattheywere generallyyounginage, highlyeducated, andeconomically well to do. The motivations for blog useincluded information seeking and checking media facts,convenience of use, personal fulllment, political andsocial surveillance, andexpressionandafliation. How-ever,oneunderlyingfactorthroughoutthesixmotivationswaspolitical involvement. Althoughthesurveywascon-ductedforblogusersingeneral,itdisclosedthetrendthatblogs were basicallya mediumfor political informationseekingandparticipation(Kaye2005).McKenna andPole(2007) report that thecontents ofpolitical blogs are composedof informationabout newsarticlesfromthemassmedia,introductionstootherblogspostings, and criticismabout mass media coverage onpolitical affairs. Content that promotes political activism orideological issues are very rare. McKenna (2007) alsoreportedthat political bloggersconstruct their blogsfromthemotivationof voluntarism(laborsof love) andnotforcommercialpurposes.3.2 HyperlinkinginpoliticalblogosphereThestructureofthepolitical blogospherereectsaset ofrelationships among political role takers including citizens,politicians, parties, media or civic organizations (Park et al.2004), that is, their social network. Adamic andGlance(2005) studied the linking patterns and discussion topics ofpolitical bloggers. After examining the posts of 40 A-listblogstostudyhowoftenpoliticalbloggersreferredtooneanotherovera2-monthperiodpriortotheUSpresidentialelection of 2004, they found that liberals and conservativeslinkprimarilywithintheirseparatecommunities, withfarfewer cross-links exchanged between them. Also, theyfoundthat thetwogroups of blogs focusedondifferentnewsarticles,topics, andpoliticalgures.AsAdamicandGlance(2005) indicate, thebenet ofhyperlink analysis is the nding of the relational patterns incyberspace. Extant studies demonstrated the utility ofstructural hyperlink analysis of activist organizations(Adamic 1999; Rogers and Marres 2000; Burris et al. 2000;Tateo2005;Parketal.2005;GarridoandHalavais2003).Theactivist groupsmadeuseoftheirexistenceonlineforanti-orpro-abortion, racismoranti-racism, women, envi-ronment, climatechange, andpolitical campaign(BiddixandPark2008).Political blogsrunbyindividualsarehyperlinkedwithorganizationalblogsaswellasotherindividuals.Thewebof these relationships tends to show an unequal distributionwhereasmall numberof blogsoccupyamajorityofhy-perlinks(i.e.,powerlawdistribution,Barabasi2002).Thisconcentrationalsomeansthat amajorityof blogsdonotlinkor arenot linkedbyother blogs (Adar andAdamic2005;Adaretal. 2004;WuandHuberman2004).Inaddition, political blogs tendtobeclusteredalongwith their interest or afliation. For instance, social activistblogstendtolinkotheractivist blogswithsimilarmove-ment themes. Further, they link to the political parties theysupport orcriticize. However, clustersfoundfromblogo-sphere centered around a small number of key nodes(Herringetal. 2005;Schmidt2007).This uneven distribution of links has largely twoimplications.First,multilaterallinkingpracticesconstituteacommunityofnodes(blogs)withsimilartopicsorposi-tions. Central nodes in the hyperlink network facilitatecommunication among political bloggers in the group.Also, groupnorms andrules emergeamongenthusiasticparticipants of the community. Schmidt (2007) reports thatblogcommunitiesareestablishedwithinformalrulessuchas blogetiquette, includingcreditingthesourceof alink. Headdsthatthebloggerswhoshareimplicitsocialnorms andmeet thegroups expectations areconsideredmembers regardless of the existence of the ofcial memberregistrationprocessforthegroup.Second, thenatureofblogcommunityproducesacon-centrationof hyperlinks within a fewblogs resultingincyberbalkanization, meaningthereinforcement of par-tisan discourse online. Conservative blogs tend to linkother blogs withsimilar political orientation(RepublicanPartysupporterblogsandtheRepublicanParty)comparedto Democratic ones (Adamic and Glance 2005). Thisphenomenon was also found fromliberal bloggers whoshowed salient tendency to hyperlink Democratic blogsand Web sites (Adamic and Glance 2005; Park et al. 2005).Ontheotherhand, Hargittaietal. (2005)criticizedthebalkanization argument. They found that blogs linked otherWebsites withdissimilar ideological orientationintheirpermanent linksection. Further, bloggers linkedexternalinformationsourceswithdissimilarattitudes intheirposts.In a similar vein, hyperlinking does not necessarily involveideological/attitudinal similarity. Parket al. (2005) reportthat Korean National Assemblymens Web sites have morecontent-neutral navigational outlinks thanself-expressiveorpartysupportiveones. Politicianslinksourcesofinfor-mational utility rather than ideological similarity. Thisnding shows that hyperlinks are sometimes embeddedfornavigatingrelevantinformationsources.Onemorenoteworthypoint isthat hyperlinks maybeemployedtoexpress antipathytowardthe Website tar-geted. Political resistance sometimes becomes a motivationto link to a Web site. For instance, the ethno-religious34 D. Rosenetal.1 3conict between Serbs and Albanians in former Yugoslaviaused Web sites, such as http://www.alb-net.com andhttp://www.srpska-mreza.com, to release their own por-trayaloftheconictandanimosity(Sunstein 2001;Balkin2004).In addition, Lin et al. (2007) argue that hyperlinksamong blogs are empirical indicators of relationshipsbetween the cities they live in. The frequency of hyperlinksfromcityAtocityBdenotedtheperceivedimportanceofcity Bfor residents of city A. Both geographical andmental proximitywereapredictor of intercity, inter-bloghyperlinks.In sum, blog hyperlink networks showhomophily ofattitudes, information sources, and antipathy. Also, theyconrmthat social networkanalysiscanbeusedforana-lyzing linking practices and relationships online amongindividuals. Although central nodes tend to account for themajor portion of the whole network, the fact does notconsider the importance of understanding structure ofonlinerelationships.4 SocialmediaandnetworkingresearchDevelopments in information and communication tech-nologies(ICT)havetransformedtherelationshipbetweenindividualsandinformation(e.g., searchengines),andthemediationofindividualswitheachotherinamultitudeofcontexts (e.g., blogs, e-mail). From a social networkperspective, thesetransformationshaveguidedouraccessto the information and social resources that we use tonavigateour social life. Web2.0typeapplications, suchas SNSandMUVEs, havecombinedthesenewwaysofaccessing information and relational contacts to createsocio-technical networks that are both transactive andportable, andall oftheseactivitiesarepreciselyrecordedin event log data that can be extracted for networkanalysis. It is in this sense that emerging ICTs haveprovidedsome of richest behavioral andstructural com-municationnetworkdata. The followingsectionreviewssome of the main streams of investigation into socialnetworksandsocial media.Researchon CMChas seen an explosive increase inattention since the advent of Internet-based ICTs, as the useof online tools has permeated our social lives. Morerecently, researchattentionhas shiftedtowardtheuseofCMCtosupport existingrelationships, likeweblogs andSNS(boyd2007;Ellisonetal. 2007;KimandYun2007;Lackaff et al. 2009; Rosenet al. 2010; Stefanone et al.2010a). TheresearchonSNSreects ashift inthewayInternet users are afforded more ways to create and activelymanage online content, often referred to as Web 2.0(OReilly 2005). The ease and exibility of managingonline content ina social networkingsense, whencom-binedwithpersistentconnectionstoonessocialnetwork,lead to unique ways in which individuals behave online andmanagetheirresources.Traditionally, media content has been the product ofmediacompanies, but newuser-createdanduser-focusedonline platforms suchas wikis, blogs, SNS, andmedia-sharing sites allowfor an increase of individual mediaownership,andthuspersonalinvestmentinmediacontent.LenhartandMadden(2005),forexample,foundthatoverhalf of Internet-usingteenscreatecontent intheformofblogs andshare photos andvideos througha varietyofother online services such as Facebook, Flickr, andYouTube.SNSssuchasFacebookandMySpacehaveemergedasa focal point for content creationandsocial interaction.Over 98%of collegestudents haveSNSproles (PACSSurvey 2007). boyd(2007, 2008) foundthat SNSusersmodelidentitythroughsocialnetworkprolessothattheycanwritethemselves andtheir communityintobeinginnetworked publics. More specically, [a process of]articulatedexpressionsupports critical peer-basedsocial-itybecause, byallowingyouthtohangout amongtheirfriends and classmates, social network sites (SNSs) areproviding teens with a space to work out identity andstatus, makesenseof cultural cues, andnegotiatepubliclife (boyd 2007, p. 2). boyds research frequently dis-cussednotions of culture, andhowSNSs allowusers tobothlearnandperpetuateculturalnormsandcues,buthasgenerally focused on subcultures such as youth or gayculture.An SNSprovides a multifunctional platformfor per-sonal onlinecontent creation, includingphotoandvideosharing, text messaging, commentingonotheruserscon-tent,blogging,andthemainfunctionality,displayingwithwhomone is friends with. This so-called friendingallows users tovisualizetheir social networkof connec-tionsinaphoto-baseddisplay.SNSfriendshaveaccesstothecontentofeachotherspersonalprole,whichisoftennot visibletonon-friendsthroughtheuseof privacyset-tings. The prole may contain photos, videos, personalmessages posted by other friends, and other personalinformationsuchasinterestsandcontactinformation.ResearchinterestinmediatedsocialnetworksandSNSusehasgrownrecently, withtopicsincludingthestudyofonline social capital froma social support andresourceaccess perspective (Ellison et al. 2007; Lackaff et al. 2009),communicative behaviors from a social-psychologicalperspective(Stefanoneet al. 2010a) andfromaculturalperspective(Byrne2007;KimandYun2007;Rosenetal.2010), asrelatedtomediaconsumption(Stefanoneet al.2010b), and for a global cooperation network (RoyLafontaineetal. 2010).Onlinenetworks 351 34.1 SocialsupportandsocialcapitalInlight ofthegrowingpopularityofmediatedsocial net-works, mediatedsocial support hasemergedasanimpor-tant research subject. In their discussion of earlier research,Bargh and McKenna (2004) argue that CMChas littledirect impact onmeaningful social interactionwithclosefriends andfamily, andthat thereisnodecreaseintimespent with strong ties due to Internet use. Rather thansubstituting for off-line social interaction, they point toevidence that CMC is actually used to help maintainbroader social networks (cf. DiMaggio et al. 2001; Howardetal. 2001;Wellmanetal. 2001).Ellison et al. (2007) examined social capital in thecontextofSNSuse.Theysurveyedcollegestudentsabouttheir use of Facebook and measured a range of usagebehaviors, psychological traits, and social capital, andfoundapositivecorrelationbetweenFacebookparticipa-tionandmanyforms of social capital, notingthat whilegeneral Internet use did not predict access to social support(bondingsocial capital), Facebookuse was a signicantpredictor. Theynotethat thisndingwarrantstheexami-nation of the specic types of online behaviors in thesearchforexplanationsofsocialoutcomes.More recently, Lackaff et al. (2009) examinedthe ability ofSNSusers toenact social support. Their ndings presentedtherst results investigating the fundamental issue of the actualvalue of the friends in SNSas related to social capital. Theyused a two-tier, nested data collection to establish the abilityof an individualto getsomeone in theirSNSnetwork to dothemafavor. Results showedthat thecontact frequency,perceivedreciprocity, andstrengthof tie were positivelyrelated to the enacted support. Additionally, they found thatthepersonalnessof themessagewasnot relatedtosocialsupport.ThesendingsshowedthatSNSuserscouldenacttheir online social networks for social support, but it was notproportional to the size of the network, contact, or reciprocity.Also, the strength of ties are important.4.2 Contingenciesofself-worthSNSbehavior has alsobeeninvestigatedfroma social-psychological perspectiveasrelatedtoonescontingencyof self-worth(Stefanone et al. 2010b). Contingencies ofself-worth afford a more nuanced approach to variabletraits, which affect self-esteem and may help explain onlinebehavior. Findingsindicatedthat public-basedcontingen-cies explainedonlinephotosharing, whileprivate-basedcontingencies demonstrated a negative relationship withtime online. The appearance contingencyfor self-worthhadthestrongest relationshipwiththeintensityofonlinephoto sharing, although no relationship was evident fortimespentmanagingproles.4.3 CultureandgenderRosenetal. (2010)examinedtheoccurrenceofculturallyandgenderinuenceddifferencesinonlinebehavior, off-linenetworks, andsatisfaction. Resultsrevealedthat par-ticipants who identify with more individualistic culturalbackgrounds have larger networks of friends on SNSs, haveagreater proportionof thesefriends whomtheyhadnotactually met face to face (dubbed promiscuous friending byStefanone et al. 2008), share more photos online asopposedtoparticipantswhoidentifywithlessindividual-istic cultural backgrounds. Social support network size wasasignicantpredictorofsatisfactionwithlife, whileSNSnetworksize was not. Findings suggest that participantswho identify with more individualistic cultural back-grounds tend to self-promote, are better connected, andmore satised with their social lives. It seems off-linenetworks are more important thanmediatednetworks intermsofpsychologicalwell-being.Kim andYun (2007) found thata KoreanSNS reectedmanyofthecollectivisticnotionsofKoreanculture. Spe-cically, the majority of participants utilized SNS tomaintaincloserelationships withasmall number of tiesinstead of creating newconnections with people. Thendingsareinlinewithpreviousconstructionsofcollec-tivisticculture.Lenhart andMadden(2007) foundthat gender differ-encesinlanguagechoiceareclearlyobservableonSNS.Older teenage girls (ages 1517years) were more likely touseSNStostayincontactwithfriendshardlyseenfacetoface and maintain close face-to-face relationships thanteenageboys of thesameage. Older teenageboys weremorelikelythangirlsofthesameagetouseSNStoirtand make new friends (Lenhart and Madden 2007).Therefore, in SNS where social and gender context cues areavailable from posting, participants may spend timereviewingfriends sites tobetter understandwhat is thesocially appropriate presentation of themselves (boyd2008).4.4 MediaconsumptionSocial cognitive theory suggests a likely relationshipbetweenbehaviormodeledonincreasinglypopularrealitytelevision (RTV) and user behavior on SNS. Stefanoneet al. (2010b) surveyedyoungadults(N=456) todeter-minetheextent towhichRTVconsumptionexplainedarange of user behavior in the context of SNSs. Resultsshowedaconsistent relationshipbetweenRTVconsump-tionandthelengthoftimespentonthesesites,thesizeofusersnetworks, theproportionoffriendswhomtheyhadnot actuallymet face to face, and photo sharing frequency,whilecontrollingfor ageandgender. Other categoriesof36 D. Rosenetal.1 3television viewing were not related to users onlinebehavior. Findings suggesteda relationship between theconsumption of popular traditional mass media content anduseofSNSs.4.5 SNSasglobalcooperativenetworksRoy Lafontaine et al. (2010) investigated engagementactivities in an online resource exchange community,CouchSurng.com, toresearchelementssuchassenseofbelonging, connectedness,andtrustin anSNScoordinatedglobal onlinecommunity. CouchSurng.comisanonlinecommunitywhere members coordinate travel accommo-dations with other members, as well as gatherings forcultural exchange. Assuch, CouchSurng.comrepresentsanSNSwheretheexchangeofresourcespresentstangibleoff-linecommitmentsthat havecreatedaglobal coopera-tive network. Findings conrm that members who have notmet facetofacewithother membershavealower senseof belonging to the community then those who have.Increased attendance to gatherings was positively related tosenseof belongingtothecommunity, andhostinghadapositive relationshipwithtrust inthe community. Addi-tionally, CouchSurfers reportedthat theypreferredtobecontacted through personal e-mails rather then groupe-mails, although those who reported increased participationingatheringsfoundgroupe-mailstobeuseful. Auniqueelement of the CouchSurng research is that the exchangesontheSNSareconnectedtoactualoff-linecommitments,whichare generallyabsent fromall other SNSresearchwhere the implications of friending are generally limited tothe exposure to information and communicative potential.5 SocialnetworkanalysisinvirtualenvironmentsAlthoughthegraphical qualityofmulti user virtual envi-ronments (MUVEs) has been increasing in quality andapplication, theinteractionwithinthesevirtualworldshasremainedprimarilyInternet relaychat (IRC). There hasbeenanincrease inthe useof Voice over IP(VoIP) inMUVEs, but thelarger, community-orientedMUVEsstillusemainlyIRC. Usersgenerallyappearasavatars(visualrepresentation of an individual in-world) in the virtualworlds along with communicative elds, such as a text boxwheretheycanpost-commentsandtrackthediscussionofother users. Text boxes displayingIRChas beena suc-cessful tool allowing for communicative interaction.However,IRCposesadifcultyforresearchersseekingtoanalyze and interpret communicative interaction, since dataisstoredintheformof chat logsthat canoftenbethou-sandsofpages. Thecurrentsectiondiscussesthemethod-ological procedures that have been developed for therepresentationandanalysisofchat interactioninMUVEsassocialnetworks.IRCin MUVEs is conducted in a semi-synchronousway, wherecommentspostedappear almost instantlyforother userstoviewandrespondto. IRCisamuchmorereal-time mode of computer-mediated communication thanlistservmessages, bulletinboards, ande-mail. Muchlikeinstant messaging(IM), IRCallows users toselect asetusername that appears before each posted commentallowingmultipleuserstocommentandmaintainconver-sational interaction. Posts toIRCconversations aregen-erallyquiteshort, usuallyoneor twolines, allowingtheIRCinteractiontobesimilar tomulti-participant face-to-faceconversation(Paolillo1999).IRCinteraction is conducted within a chat box thatdisplays all users comments along with their username in alog le. In addition to IRCinteraction being semi-syn-chronous, it is also persistent. Since face-to-face interactionisgenerallyephemeral, itisverydifcult toreferbacktoprevious parts of the conversation for reference, somethingthat ispossibleviaIRC. Thepersistenceoftheseinterac-tionsallowforthestorageofall dataaschat logs, whichcaninturnbeusedforanalysesoftheusersinteraction.However, the nature of chat logs as a dynamic, non-threadedinteractionintroducessomemethodological hur-dles regarding network analysis. Chat sessions are stored asloglescontainingtherawchat dataincludingmetadatasuchastime-stampsanduser idsattachedtoeverycom-ment. The metadata allows for the precise tracking ofsourceandpacingofinteraction.Therehavebeenadvancementsintheanalysisof net-workedinteractioninvirtual communitiesinanumberofareas. Smith et al. have added a substantial cache ofmethodsandperspectivesbystudyingtheinteractionandstructure of Usenet (Smith 1999; Turner et al. 2006).Usenet isanonlinebulletinboard-typesystemcommonlycalled newsgroups, although they are not necessarilyassociatedwithnews, asmanynewsgroupsareforrec-reational, technical, political, andcultural topics. Oneofthebenetsof analyzingUsenet isthat theinteractionisthreaded in tree-like structures, where conversations lead tosub-threadsandcontent canbecross-postedtogetherwithnewsgroups. Thisthreadedstructureprovidesaclear dis-tinctionofwhoisrespondingtowhom,whentheresponsewas posted, and which groups are associated with theinteraction.Usingthisdata, Smithetal.haveexplicatedanumber of methods for the structural analysis of theseonline communities via their Netscan project and havewrittenoninteraction, participant contribution, andnews-grouphierarchies.Smith has also investigated the social life of smallgraphical chat spaces by analyzing Microsofts V-Chatsystems (Smith et al. 2000). The V-Chat research illustratesOnlinenetworks 371 3theusagepatternsofgraphical chat systems, illuminatingthe ways physical proxemics are translated into socialinteractions in online environments. Krikorian et al. (2000)developedmethods tostudyuser proximityingraphicalchat rooms and found that various perceived demographicsinuenced the social distance of avatars in the graphicalchatenvironment.In addition to the structural analysis, there have also beenanumber of methodological advancements regardingthecommunicative content of virtual environments. Sack(2000) generated conversation maps of newsgroup postingsand described very large conversations by visualizing largeamounts of interaction in newsgroups. Suthers et al. (2010)developed a framework for representing and analyzing dis-tributedinteractionwithinMUVEs, includingsomestruc-tural representationofinteractioninsequential recordsofevents. However, themethodsdevelopedweremicroana-lytic and have yet to be employed on large-scale data. Rosenet al. (2003) explicated a methodology for semantic networkanalyses of IRCinteraction in MUVEs, representing amethodological advancement in the quantitative analysis ofthecontent of IRCinteraction. However, therehadbeenlittletonodevelopment ofmethodstoextract social net-worksfromIRCinteractionuntilRosen(2010)andRosenandCorbit (2009) developednetworkanalytictechniquesfor the measurement and representation of networks inIRC-based MUVEs. Many of these techniques map, display,and study thread-based online communities, such as Usenetgroups; whereas graphical chat rooms sequentially log chatinteraction, whichis difcult toseparate andanalyzeassub-groups, parsed interaction, or as a structural system.Even though Rosen et al. (2003) analyzed the content ofIRC interaction in MUVWs using semantic network analy-sis, there still remained a gap in procedures to extractstructuralsocialnetworksfromIRC. Manyoftheparallelonline community(e.g., Usenet) andsocial media (e.g.,SNS) research streams have beneted from structural anal-ysisandsocial networkrepresentation, but interactionviaIRC is still one of the most common forms of interaction in avarietyofcontexts(i.e., onlinegaming, educational envi-ronments), yet the structure still remains cloaked behind theformoflogledatausedtostoreIRC.Understandingthestructure of the interaction provides an in-depth and uniquewindowintoMUVWsalongseveral lines. First, networkposition can be used to identify network roles such as,similar toTurner et al. (2006), identifyingroles suchasanswerpersonandquestionperson. Second, networkana-lytictechniquescanbeemployedinthesubsequent data.Finally, networkvisualizationscanbegeneratedallowingforvisualandrepresentationalanalyses(seeFig. 3)ofele-ments that are traditionally important to community research(Preece and Maloney-Krichmar 2005).Fig.3 Networkrepresentationof IRC-basedinteractioninMUVE.Thecolorofthenodesindicatesthedifferent typesofusers(inthisexample, students nodes are lighter shadedandmentor nodes aredarker shaded). Thethickness of thelineconnectingtwonodes isproportional to the connection strength between the two nodes. Labelsindicate the case-specic roles lled by eachindividual (specicsnotpertinenttothisreview)38 D. Rosenetal.1 36 FuturedirectionsandimplicationsfornetworkscienceThescienceofsocial networkshasprogressedinparallelwiththeuseof computer andinformationsystems. Mea-suringinformationowshas beenoneof themainchal-lenges of communication network analysis, and thedevelopment of informationsystems has providedsocialnetwork scientists with a precise representation of suchows and the ability to advance the state of science.Additionally, theincreasedtheoretical understandingandanalyticrepresentationof computer andinformationsys-tems provided developers with a greater sense of howpeople and social organizations utilize technology tomanagetheresourcesembeddedintheir social networks.Thescienceof social networkanalysis andthedevelop-ment ofinformationsystemshaveco-evolvedascatalystsof each others development and advancement, and thefutureofbothisinexorablybound.Precisemeasuresofsocial networkshavemirroredtheincreased use of ICTs. From microblogging networks (e.g.,Twitter) toglobal cooperationnetworks (e.g., CouchSur-ng), thebehaviors, material, andnon-materialexchangesof individuals and larger social systems are recorded,presentingawealthofdata. Fromascienticperspective,the implications of havingaccess tosomuchdata of avarietyofformsarefourfold.First,wenowhaveaccesstomeasurements of social network relations that are morereliable than in the past. Second, the nature of the relationaldatais unique, allowingfor theanalytical explorationofnetworkstructuresinwaysthatpushtheboundariesofthescience. Third, analysis of networkevolutionis increas-ingly possible as much of the data available through ICT isdynamic, ahistoricallypersistent challengewhencollect-ingnetworkdata. Fourth, dynamicnetworkdatahascon-tributedtomethodsfortheanimationofsocialnetworks.Reliabledataarethefoundationof most science, butcompleteandreliabledataareparticularlyimportant forthe analysis of social networks. It is paramount thatresearchers are able to capture accurate and completenetworkdata, sincenetworkcompositionandowcanbegreatlychangedbytheremoval or inclusionof anypar-ticularnode.Therehasbeenaoodofavailableaccurate,large, complete data from several sources. First, developersandbusinesses (e.g., Twitter) are makingtheir data setsavailable to social network scientists, data that containprecise traces of activity among entire populations. Second,researchersareutilizinginformationtechnologytocapturemultiplexowdata fromtheir samples that allowfor amultitudeof networkanalytics. Thecombinationof pub-licly available large-scale data sets with precisely recordedcase-study data enables collaboration and validation of datacollection, recording, andanalysistechniques.Uniquedataallowascienticeldtogrowitsfounda-tionandexpanditsreach,andthescienceofnetworkshasseen an explosion of newdata forms. Fromgeospatialnetworkdatatosmall-worlddiffusionnetworks, thecom-plexityof relationspossible in the social andnatural worldposes many opportunities to network researchers. Infor-mation technology has enabled much of the data explosion,but has also provided a parallel benet in that scientists arebetter abletocollaborateonnewmethods anddistributetheir ndings (and data) very rapidly. The very distributionoftoolsandthecitationofresearchhavebecomeavalu-ableformofuniquenetworkdata.Dynamicdatahavebeenadesirefornetworkscientistssincethebirthof theeld. Amajorityof social networkarticles have statedthe needfor dynamic data, a futuredirection for, or a drawback to their current research.Indeed, dynamic data are very difcult to collect andanalyze, especiallyfor matrixalgebraic techniques. Yet,recent developments inmethods andavailabledatahaveenabledarapidgrowthintheeldof dynamic networkanalysis (Snijders 2005). Tools such as Siena (Snijderset al. 2005) and SoNIA(McFarland and Bender-deMoll2007), alongwiththemethodsaffordedbydynamicnet-work analysis packages, have allowed the state of thesciencetodevelophandinhandwiththetorrent ofover-time data available. Many of the computer-mediated formsofcommunicationhaveatimestampembeddedinthelogles,andallowforthepreservationofthedynamicnatureofsocialinteractioninsteadofforcingthecompressionofthedataintoacross-sectional aggregateof data. Visuali-zationtechniques havealsobeenrapidlyimproving, andwithdynamicdatacomeanimation.Animation of social networks is the most recent advanceinthevisualrepresentationofnetworkdata. Visualrepre-sentations of networks have aided in the analysis andelaborationof social networkssincetherst sociograms.Until recently, most visualizations representedasingularwindow into the network, being either a representation at asinglemoment oftimeorasummationofaspecicwin-dow of time. Now tools combined with dynamic data allowfor afull animationof networkevolution(Moodyet al.2005; Elbirt andBarnett 2006). It isinthisareathat net-workscienceis makingits biggest advances. As theop-erationalization of systems theory, network science isincreasinglyable toelaboratethe evolutionarynature ofsocial systems. For example, Elbirt and Barnett (2006)haveanimatedtheevolutionofanumberofdifferentnet-works includingtheinternational telephonenetwork, theCanadianmigrationnetworkandtheUSsenaterevealinguniquepatternsthat aredifcult toseeincross-sectionalrepresentations.Usingadifferentapproach,McCullohandCarley(2008) useFourier transforms toenabledynamicanimationofoncellphonenetworkdata.Onlinenetworks 391 3Thefutureofnetworkanalysistrulyliesinthenetworkitself: inthenetworkof researchers developingtheana-lytics that reveal new structures and images, in the networkof developers who use these new windows to enable a newfrontier of webtools andsocial affordances, andinthenetworksthatmakeupourlives.Everythingwedoinoursociallivesisinescapablyembeddedinthelargernetworkofinteractionsofeveryoneelseandthoseconnectionsareonlycloakedbytheabilitytosensethem. Networks areshowingusthatweareallpartofthesameeverchangingandevolvingsystem,adynamicwholeness,andthevisualandempiricalknowledgeofthoseconnectionsmaybethemostlikelytooltouniteus.Open Access This article is distributed under the terms of theCreativeCommonsAttributionNoncommercial Licensewhichper-mits anynoncommercial use, distribution, andreproductioninanymedium, providedtheoriginalauthor(s)andsourcearecredited.ReferencesAdamic LA(1999) The small worldWeb. In: Proceedings of 3rdEuropean conference of research and advanced technologyfor digital libraries, ECDL. http://www.hpl.hp.com/shl/papers/smallworld/smallworldpaper.html.Accessed3May2006AdamicL, GlanceN(2005)Thepoliticalblogosphereandthe2004U.S. election: divided they blog. http://www.blogpulse.com/papers/2005/AdamicGlanceBlogWWW.pdf. Accessed 12 Oct2007Adar E, Adamic LA(2005) Tracking information epidemics inblogspace. 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