Detecting Leaders in Behavioral NetworksIlham Esslimani, Armelle Brun, Anne Boyer

KIWI Team, LORIA, Nancy University615 rue du Jardin Botanique, 54600 Villers-Les-Nancy, France

{ilham.esslimani, armelle.brun, anne.boyer}@loria.fr

Abstract—The development of the Web engendered the emer-gence of virtual communities. Analyzing information flows anddiscovering leaders through these communities becomes thus, amajor challenge in different application areas. In this paper,we present an algorithm that aims at detecting leaders in thecontext of behavioral networks. This algorithm considers thehigh connectivity and the potentiality of propagating accurateappreciations so as to detect reliable leaders through thesenetworks. This approach is evaluated in terms of precision usinga real usage dataset. The results of the experimentation show theinterest of our approach to detect TopN behavioral leaders thatpredict accurately the preferences of the other users. Besides, ourapproach can be harnessed in different application areas caringabout the role of leaders.

Index Terms—behavioral leaders, behavioral networks, navi-gational patterns, preference propagation

I. INTRODUCTION

With the heightened evolution of the Web, e-commerceapplications and on-line social networks, understanding infor-mation flows and influence phenomenon is crucial. Indeed,the success of Web 2.0 environments as social networks,blogs, wikis, etc. led to the emergence of virtual communities.Studying and analyzing leadership and detecting leaders orinfluencers through these communities becomes thus, a majorchallenge.

Originally, leadership studies have been linked to the areaof marketing. The objective consisted in the analysis ofthe “word-of-mouth” phenomenon and its impact on markettrends. Indeed, usually, a small portion of a community hasan important influence on opinion and decision making of therest of the community [1]. This portion represents the opinionleaders.Therefore, in the area of marketing, detecting opinion leadersallows the prediction of future decision making (about prod-ucts and services), the anticipation of risks (due for exampleto negative opinions of leaders) and the follow-up of thecorporate image (e-reputation) of companies.

Furthermore, studying leadership is also relevant in otherapplication areas, such as: social network analysis and recom-mender systems. In the area of social networks analysis, detec-tion of leaders represents a great perspective. It aims notablyat analyzing influences between entities with the objectiveof studying social networks properties and predicting theirevolution. In the area of recommender systems, generatingrecommendations based on leaders, leads to an improvementof user satisfaction and ensures his loyalty.

In this paper, we are interested in studying behavioral leaderdetection in the context of behavioral networks [2], whereusers are connected when they have a similar navigationalbehavior.We consider a behavioral leader as a user who is potentiallylinked to many users with the same behavior and who can alsopropagate accurately the preferences towards these users.

Thus, we propose an algorithm that aims at detectingleaders in behavioral networks. This algorithm relies on theidentification of potential behavioral leaders based on theirhigh connectivity in the behavioral network. Then, leaderpreferences are propagated to the neighbors through the net-work. So as to detect actual behavioral leaders, the propagatedpreferences are evaluated in terms of precision. The higher theprecision ratios are, the more reliable the behavioral leadersare.Our algorithm can be involved in the frame of differentapplication areas, as those cited above: marketing, socialnetwork analysis and recommender systems.

The remainder of this paper is organized as follows. In thesecond section related works regarding detection of leadersare presented. The third section focuses on the descriptionof our proposed algorithm related to the selection and thedetection of behavioral leaders. The fourth section is dedicatedto the presentation of the experimentation we conducted so asto evaluate the performance of our approach. The results ofthis experimentation are presented in the same section. Finally,we summarize our research and conclude with some possiblefuture directions.

II. RELATED WORKS

The issue regarding our research is to find a reliablemethod for detecting behavioral leaders. As related works, wepresent here some research studies investigating the problemof detecting leaders in different application areas.

Leadership and influence propagation have been subject ofmany studies in the area of marketing, social science andsocial network analysis [3]. Researchers tend to understandhow communities start, what are their properties, how theyevolve, what are the roles of their members and how in-fluencers and opinion leaders can be detected through thesecommunities. Katz and Lazarsfeld [4] defined opinion leadersas ”the individuals who were likely to influence other personsin their immediate environment“.

The earliest studies of influence and leadership focused onthe analysis of the propagation of medical and technological

2010 International Conference on Advances in Social Networks Analysis and Mining

978-0-7695-4138-9/10 $26.00 © 2010 IEEE

DOI 10.1109/ASONAM.2010.72

281

2010 International Conference on Advances in Social Networks Analysis and Mining

978-0-7695-4138-9/10 $26.00 © 2010 IEEE

DOI 10.1109/ASONAM.2010.72

281

2010 International Conference on Advances in Social Networks Analysis and Mining

978-0-7695-4138-9/10 $26.00 © 2010 IEEE

DOI 10.1109/ASONAM.2010.72

281

innovations [5]. More recently, [6] also examined this questionand proposed diffusion models of innovations in networks.

In the area of marketing (viral marketing), influence propa-gation is often linked to the word-of-mouth phenomenon andits effects on the success of new products [7]. The most im-portant challenge in marketing is how to find a small segmentof the population (influencers or leaders) that can influence theother segments by their positive or negative opinions regardingproducts and services [1]. Keller and Berry [8] confirm theimportance of influencers as they guide the decisions of acommunity and predict market trends. According to theirstudy, “one American in ten tells the other nine how to vote,where to eat and what to buy”.

With the development of Internet, leaders and influencersdo not use only traditional word-of-mouth, they can prop-agate their opinions based on interactive exchanges throughblogs, forums, wikis and various social networks platforms. Asocial network represents a social structure between actors.It indicates the ways they are connected through varioussocial relationships as friendship, co-working or informationexchange [9]. Nowadays, social networks become the mostimportant medium for propagating information, innovation andopinions.

Several recent studies have been interested in analyzinginteractions and influences between entities and examining theimpact of leaders in social networks. [10] study approxima-tion algorithms for influence maximization in co-authorshipnetwork. [11] show how to identify active and non active in-fluential bloggers that can lead trends and affect group interestsin the blogosphere. [3] propose a pattern mining approachto discover leaders and to evaluate their influence in socialnetworks. Actions such as tagging, rating, buying and bloggingare considered in frequent pattern discovery. [3] consider infact that in a social network, a leader can guide the trends ofperforming actions. Thus, friends are tempted to perform thesame actions than the ones the leader performed. An approachbased on text mining and social network analysis is presentedin [12]. This approach consists in detecting opinion leadersbased on mining users’ comments about products and miningcommunication relationships among them.

Other studies investigated the role of network structure onthe propagation of information and opinions. Some of them[13] [14] emphasize the role of highly connected nodes in asocial network, called also hubs, in information disseminationand evolution of collaboration in this network. [15] confirmthat highly connected nodes have an important influence ontheir neighbors. Keller and Berry [8] show also that users whoinfluence others, have a relatively large number of social links.

In this paper, by considering social networks approaches,we aim at detecting leaders among users by harnessing thebehavioral information and by considering the structure ofusers network. The following section describes our proposedapproach.

III. LEADER DETECTION AND PREFERENCE PROPAGATIONIN BEHAVIORAL NETWORKS

In this paper, we propose a novel approach to identify lead-ers through a behavioral network. This network is constructedbased on behavioral information and users are connected ifthey share similar navigational patterns. Then, by consideringhighly connected users as potential behavioral leaders, leaderappreciations are propagated in this behavioral network. Thebest accurate the propagated appreciations are, the more ef-fective the leaders are. The following subsections describebehavioral networks modelling and present the algorithm wepropose to detect behavioral leaders.

A. Construction of the Behavioral Network

A behavioral network is constructed based on behavioralinformation, where users are linked as they share similarnavigational patterns. This approach exploits behavioral datawith the objective of assessing similarities between users.Behavioral data refers to usage traces capturing navigationalactivities and interactions of users on a given website.

We consider that two users ua and ub, who share commonsequential patterns are similar. The longer the sequence of acommon pattern is, the more the users are similar. Therefore,our goal is to identify for every pair of users (ua, ub), themaximum length LKmax(ua, ub) of a navigational patternamong their navigational common patterns.

Then, the similarity of navigation between two users iscomputed by using formula (1) that takes into account com-mon patterns between the active user ua and the neighboruser ub. This formula computes, for each pair of users ua

and ub the similarity of navigation SimNav(ua,ub) as theratio of the maximum length of a common frequent patternLKmax(ua, ub) and the minimum of maximum sizes of ua andub sessions denoted SessMax(ua) and SessMax(ub). Let usnote that the common frequent pattern is intra-session.

SimNav(ua,ub) =LKmax(ua, ub)

min(SessMax(ua), SessMax(ub))(1)

We use the minimum of maximum sizes of sessions inthe denominator so as to avoid to penalize a new user whohas few sessions with short sizes. We note that the similarityvalue is normalized between 0 and 1. This metric emphasizesthe importance of the longest frequent patterns to evaluatesimilarities of users. The higher the length of a sequentialpattern is, the more the users are similar.

Once navigational similarities are evaluated, we build thebehavioral network by using a graph G = (N,E) where nodesN represent users, edges E represent the links between usersand the navigational similarities are the weights of the edges.

B. Detecting leaders in the Behavioral Network

In the frame of the constructed behavioral network, we aimat discovering reliable leaders. Thus, we propose an algorithmthat considers the high connectivity to select potential leadersthat propagate, in a following step, their preferences in the

282282282

network. Then, by assessing the quality of these propagatedpreferences, actual leaders are detected.

In social networks, the detection of leaders relies on theanalysis of the social links through the network. Here, weemphasize the role of behavioral links to identify behavioralleaders in a network.

According to [13] [14] [15] [8] mentionned in section II,we define a behavioral leader as a user who is not onlyhighly connected in the behavioral network. He has also ahigh potentiality of predicting the preferences of the otherusers. We assume in fact that a behavioral leader can propagatehis preferences in the network. We propose to propagateappreciations with an attenuation factor. This factor is directlyrelated to the similarity between users (the weights of thelinks). Indeed, when users are very similar, there is a highprobability that they share the same appreciations about items.

Moreover, we assume that the items that a behavioral leadercan propagate, are the items he prefers. Since our model relieson usage traces, the estimation of user appreciations (“like” or“dislike” an item) is required. To distinguich preferred itemsfrom non preferred ones, we take into account two implicitparameters: frequencies of visiting an item and duration ofvisiting an item that we can compute based on extractedinformation from web server log files [16].

Algorithm 1 represents our proposed algorithm for detectingbehavioral leaders. This algorithm uses as input the graphG = (N, E) modelling the behavioral network where nodesN represent users and edges E are the links between them.Our algorithm includes two main steps. Let us notice thateach step considers a distinct set of items denoted Itr andIts. Itr refers to the items used (at the training step) to assessbehavioral similarities and construct the behavioral networkand Its represents the set of items considered to validate theactual behavioral leaders (the test step).

At the first step of algorithm 1 (“function SelectPotential-Leaders”), for each node ua in the graph G, the connectivity(degree centrality) is computed as the number of links (neigh-bors) incident upon ua. Then, TopN potential leaders UPL

are selected based on their high connectivity in the behavioralnetwork.

At the second step of algorithm 1 (“functionDetectLeaders”), for each potential leader upl ∈ UPL,their preferred items are identified Iprf (upl) ⊂ Its. Then,as presented in formula (3), potential leader appreciationsapr(upl, ij) about items ij (ij ∈ Iprf (upl)) are propagatedto their direct neighbors such as a propagated appreciation,denoted papr(upl, ij), from a leader upl to the neighbor nodeua about an item ij is weighted by the coefficient α(ua,upl).As presented in Figure 1, the weights α range from 0 to 1according to the similarity between upl and ua computed byformula (1).

Once appreciations are propagated to a neighbor ua, theyare evaluated in terms of precision using formula (4). Thisprecision is calculated as the ratio between Nrl representingthe number of propagated items that are relevant for ua (that

are really appreciated by him) and Nr representing the numberof all propagated items (relevant and irrelevant). Then, aspresented in formula (5), for each potential behavioral leaderwe evaluate the precision p(upl) as the average of precisionscomputed over all his neighbors ua. We note that m representsin formula (5) the number of upl neighbors.Precision ratio highlight finally the actual behavioral leadersover the network. The higher the precision ratio is, the morereliable the behavioral leader is.

Algorithm 1 Detection of behavioral leaders1: function SELECTPOTENTIALLEADERS2: for each node ua over the graph G do3: Evaluate “Degree centrality” D(ua) . denoted

|Γ(ua)|D(ua) = |Γ(ua)| (2)

4: end for5: Sorting Degrees D of all nodes N in a descending

order6: return TopN potential behavioral leaders UPL with

high Degrees of centrality7: end function

8: function DETECTLEADERS9: for each potential behavioral leader upl ∈ UPL do

10: Select preferred items Iprf (upl) ⊂ Its

11: Select neighbor nodes12: for each selected neighbor ua do13: for each item ij ∈ Iprf (upl) do14: Propagate appreciations apr(upl, ij) to ua

such as:

papr(ua, ij) = α(ua,upl) ∗ apr(upl, ij) (3)

15: Evaluate precision of each papr(ua, ij)(papr(ua, ij) is relevant or not for ua)

16: end for17: Evaluate precision of all propagated apprecia-

tions to ua

p =Nrl

Nr(4)

18: end for19: Evaluate precision of the potential leader upl as the

average of precisions p computed over all his neighbors

P (upl) =

∑mua=1 p

m(5)

20: end for21: return TopN actual behavioral leaders UL with the

best ratios of precision22: end function

283283283

Fig. 1. Weights values depending on similarities

0.8 – 1 0.5 – 0.8 0.2 – 0.5 0 – 0.2

Similarity value

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

1,1

Weig

ht

(α v

alu

e)

IV. EXPERIMENTATION

A. Dataset

So as to evaluate the performance of our approach, weuse a real usage dataset extracted from the Intranet of CreditAgricole Banking Group, in particular the usage data relatingto the Department of Strategies and Technology Watch. Allthe users are members of the Group and can access numerousresources such as: news, articles, faq, special reports, etc.

Thus, we exploit the usage data that represents the naviga-tional activities of users on the Intranet. This data has beenstored in Web server log files. It contains mainly informationabout user-ids, session-ids and time of starting and endingsessions. The studied dataset is related to 748 users and 3856resources. It has been split into 80% and 20% correspondingrespectively to training and test datasets.

So as to evaluate the quality of the propagated appreciationsthrough the network, we extracted leaders preferences aboutitems denoted Iprf(ul) from the test set Its. As mentionnedin section III-B, we considered only positive appreciations ofthese leaders (the items that they like). Besides, the weightsfrom formula (1) and presented in Figure 1 are used asthe attenuation factor at the propagation step. For example,similarity values in the range ]0.8−1.0] have a correspondingα weight equal to 1.0.

B. Evaluation

The evaluation of precision can rely on statistical or decisionsupport measures [17] [18]. When it is statistical, an accuracymeasure evaluates the deviation between propagated appreci-ations and the real preferences that are actually assigned byusers to items. When it is a measure of decision support, itevaluates the relevance of a set of propagated appreciationsby computing the proportion of items that the user consideractually useful and relevant [19]. Precision is widely used asa measure of decision support. Let us notice that here binarypreferences are considered to distinguish relevant items fromnon relevant ones.

C. Results

This evaluation aims at examining the effectiveness of ourapproach for detecting behavioral leaders in the behavioral net-work. Thus, in this experimentation, we evaluate the precisionof propagated appreciations regarding each potential leaderbased on formulas (4) and (5).

Figures 2 and 3 present the distributions of the number ofpotential behavioral leaders according to the precision whenwe take respectively into account 10% and 20% of TopNpotential behavioral leaders at the propagation step. Let usnotice that for about 53% of TopN10 behavioral leaders and49% of TopN20 behavioral leaders, precision can not beevaluated due to one of the following reasons:

• The items propagated by potential behavioral leaders havenot been viewed yet by the neighbors. Thus, we can notexamin if the concerned potential leaders are actual ornot.

• The potential behavioral leaders have no positive appre-ciations (in the test set Its). So, they can not propagatetheir preferences to the neighbors.

We note that in the results presented here, this category ofbehavioral leaders is not considered.

Now, by observing the results of Figures 2 and 3, wecan see that precision ditributions have a similar evolutionfor TopN10 and TopN20. Let us notice that TopN10 andTopN20 correspond respectively to about 53 and 101 potentialbehavioral leaders among all the users in the studied dataset.When TopN10 behavioral leaders are harnessed, we observethat 80% of these leaders have more than 60% of precision,60% have a precision higher than 80% and 40% reach 100%.Regarding TopN20 behavioral leaders, we can see that simi-larly about 80% leaders propagate accurately their apprecia-tions as the corresponding precision is higher than 60%, 53%have a precision higher than 80% and 37% reach 100% ofaccuracy.

So, when using either TopN10 or TopN20, an importantproportion of potential behavioral leaders have a high precisionof propagated appreciations. We consider that the leaders thatreached a precision higher than 80%, represent the prominentnodes among all the nodes in the behavioral network.

Moreover, in this experimentation we compared the per-formance of our approach to the standard CF [20] in termsof precision. We note that the standard CF exploits here thePearson Coefficient as a similarity function to detect neighborusers, that are involved in the prediction process. Table Ipresents the precision averages corresponding to our approach(Leaders based preference propagation) and to the standardCF. These precisions have been computed over the same pairsof < user, item > when using two sets R1 and R2. Thesesets correspond respectively to the pairs of < user, item >considered at the propagation step by TopN10 and TopN20leaders.

By observing the results in Table I, we can see that, on theset of items predicted by the leaders, our approach outperformsthe standard CF as a higher accuracy is reached.

Indeed, when we consider the sets R1 and R2, about 77%of precision is attained. At the opposite, the standard CF isless accurate as the precision averages reach only 51% and43% when R1 and R2 are respectively considered. Thus,these results confirm the effectiveness of behavioral leadersregarding the proposition of relevant items to the other users.

284284284

TABLE IPRECISION AVERAGE OF LEADER BASED PREFERENCE PROPAGATION

COMPARED TO THE STANDARD CF

Model R1 R2

Leader basedpreference

propagation

77% 76%

Standard CF 51% 43%

So, overall, the results presented here show the interest ofour approach to detect reliable behavioral leaders in behavioralnetworks. These leaders have in fact an important potentialityof proposing relevant items, as a high accuracy is reachedby most of them. Thus, they represent the entry nodes inthe behavioral network. Indeed, they can be considered forexample in marketing area, to push new products and servicesto the community.

Fig. 2. Distribution of TopN10 potential behavioral leaders according toprecision percentage

0-40% 40%-60% 60%-80% 80%-100% 100,00%

Precision percentage

0

2

4

6

8

10

12

Num

ber

of

pote

nti

al behavio

ral le

aders

Fig. 3. Distribution of TopN20 potential behavioral leaders according toprecision percentage

0-40% 40%-60% 60%-80% 80%-100% 100,00%

Precision percentage

0

5

10

15

20

Num

ber

of

pote

nti

al behavio

ral le

aders

V. CONCLUSION AND FUTURE WORK

In this paper we presented a new approach that aims atdetecting leaders within the framework of behavioral networks.In these networks, users are connected when they have similarnavigational behaviors. The detection of leaders relies ontheir high connectivity in these behavioral networks and theirpotentiality of propagating accurate appreciations.

This approach is evaluated in terms of precision using a realusage dataset. The experimentation highlights the importanceof considering TopN behavioral leaders through the networkso as to propagate accurately the preferences to the other users.Indeed, a high accuracy of propagated preferences is reachedcomparing to the standard CF.

Our approach can be harnessed in different applicationareas, attaching importance to the role of leaders, as marketing,social network analysis or recommender systems.

As a future work, we plan to experiment additional datasetsso as to validate the generalization of our approach. Moreover,we plan to solve the problem of coverage in the frame of ourapproach. Besides, we plan to investigate other methods fordetecting leaders and analyze their performance.

REFERENCES

[1] D. J. Watts and P. S. Dodds, “Influentials, networks, and public opinionformation,” Journal of Consumer Research, vol. 34, no. 4, pp. 441–458,2007.

[2] I. Esslimani, A. Brun, and A. Boyer, “From social networks to behav-ioral networks in recommender systems,” in Proceedings of The 2009International Conference on Advances in Social Networks Analysis andMining (ASONAM). IEEE Computer society, 2009, pp. 143–148.

[3] A. Goyal, F. Bonchi, and L. V. Lakshmanan, “Discovering leaders fromcommunity actions,” in Proceeding of the 17th ACM conference onInformation and knowledge management (CIKM’08). New York, USA:ACM, 2008, pp. 499–508.

[4] K. Elihu and P. F. Lazarsfeld, Personal Influence; the Part Played byPeople in the Flow of Mass Communications. Free Press, 1955.

[5] J. Coleman, H. Menzel, and E. Katz, Medical Innovations: A DiffusionStudy. Bobbs-Merrill Co., 1966.

[6] T. W. Valente, Network models of the diffusion of innovations. HamptonPress, 1995.

[7] P. Domingos and M. Richardson, “Mining the network value of cus-tomers,” in KDD ’01: Proceedings of the seventh ACM SIGKDDinternational conference on Knowledge discovery and data mining.New York, NY, USA: ACM, 2001, pp. 57–66.

[8] E. Keller and J. Berry, The influentials. Simon and Schuster Ed., 2003.[9] M. Jamali and H. Abolhassani, “Different aspects of social network

analysis,” in Proceedings of the 2006 IEEE/WIC/ACM InternationalConference on Web Intelligence (WI’06). Washington, DC, USA: IEEEComputer Society, 2006, pp. 66–72.

[10] D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread ofinfluence through a social network,” in Proceedings of the ninth ACMSIGKDD international conference on Knowledge discovery and datamining (KDD’03). New York, NY, USA: ACM, 2003, pp. 137–146.

[11] N. Agarwal, H. Liu, L. Tang, and P. S. Yu, “Identifying the influentialbloggers in a community,” in Proceedings of the international conferenceon Web search and web data mining (WSDM’08). New York, NY, USA:ACM, 2008, pp. 207–218.

[12] F. Bodendorf and C. Kaiser, “Detecting opinion leaders and trends inonline social networks,” in Proceedings of the 2nd ACM workshop onSocial web search and mining (SWSM’09). New York, USA: ACM,2009, pp. 65–68.

[13] A. L. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, andT. Vicsek, “Evolution of the social network of scientific collaboration,”Physica A, vol. 311, no. 3-4, pp. 590–614, 2002.

[14] E. Newman, “Clustering and preferential attachment in growing net-works,” Physical Review Letters, vol. 64, no. 025102, 2001.

[15] G. Malcolm, The Tipping Point: How Little Things Can Make a BigDifference. New York: Little Brown, 2000.

[16] I. Esslimani, A. Brun, and A. Boyer, “A collaborative filtering approachcombining clustering and navigational based correlations,” in Proceed-ings of the 5th International Conference on Web Information Systemsand Technologies (WEBIST), Lisbon, Portugal, 2009.

[17] C. Ziegler, S. McNee, J. Konstan, and G. Lausen, “Improving recom-mendation lists through topic diversification,” in Proceedings of the 14thinternational conference on World Wide Web (WWW’05). New York,USA: ACM, 2005, pp. 22–32.

[18] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluatingcollaborative filtering recommender systems,” ACM Trans. Inf. Syst.,vol. 22, no. 1, pp. 5–53, 2004.

[19] S. Anand and B. Mobasher, “Intelligent techniques for web personal-ization,” Lecture Notes in Artificial Intelligence, vol. 3169, pp. 1–36,2005.

[20] J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, “An algorithmicframework for performing collaborative filtering,” in Proceedings of the22nd annual international ACM SIGIR conference on Research anddevelopment in information retrieval (SIGIR’99). New York, USA:ACM, 1999, pp. 230–237.

285285285