Measurement and Analysis of Online Social Networks Alan Mislove,Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, Bobby Bhattacharjee Presented.

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  • Slide 1
  • Measurement and Analysis of Online Social Networks Alan Mislove,Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, Bobby Bhattacharjee Presented by Aleksandra Potapova
  • Slide 2
  • Focus graphs of online social networks how they were obtained how they were verified how measurement and analysis was performed properties of obtained graphs why these properties are relevant
  • Slide 3
  • What was studied? Flickr YouTube LiveJournal, Orkut
  • Slide 4
  • Why should we perform measurements and analysis in social networks? To design future online social network based systems To understand the impact of online social networks on the Internet To reduce the number of spam To improve security aspect
  • Slide 5
  • 5 Summary of graph properties small-world power-law scale-free correlation between indegree and outdegree large strongly connected core of high-degree nodes surrounded by small clusters of low- degree nodes
  • Slide 6
  • Crawling Algorithms for large graphs BFS and DFS Snowball method(crawling only small subset of a graph by ending BFS early): Partial BFS craws overestimate node degree and underestimate the level of symmetry. In social networks, they underestimate the power- law coefficient, but closely match other metrics such as overall clustering coefficient.
  • Slide 7
  • How social networks should be crawled? The focus of the paper WCC Forward and reverse links should be used
  • Slide 8
  • How the graphs were obtained? API users groups forward/backward links HTML Screen Scraping
  • Slide 9
  • How to Verify Samples 1.Obtain a random user sample LJ: feature which returns 5,000 random users Flickr: random 8-digit user id generation 2.Conduct a crawl using these random users as seeds 3.See if these random nodes connect to the original WCC 4.See what the graph structure of the newly crawled graph compares to original
  • Slide 10
  • Crawling Concerns FW links no effect on largest WCC
  • Slide 11
  • 11 Crawling Concerns FW links increasing the size of the WCC by starting at a different seed
  • Slide 12
  • 12 SiteYTFlickrLJOrkut Users(mill)1.11.85.23 Links(mill)4.92272223 symmetry79.1%62.0%73.5%100.0% Access (FW: Forward- only) (SS: HTML screen- scraping) API (users only) FW SS for group info API (users + groups) FW API (users + groups) FW + BW SS for users + groups
  • Slide 13
  • 13 Link Symmetry even with directed links, there is a high level of symmetry possibly contributed to by informing users of new incoming links makes it harder to identify reputable sources due to dilution
  • Slide 14
  • 14 Power-law node degrees Orkut deviates: only 11.3% of network reached (effect of partial BFS crawl Snowball method) artificial cap of users number of outgoing links, leads to a distortion in distribution of high degrees differs from Web
  • Slide 15
  • 15 Power-law node degrees
  • Slide 16
  • Slide 17
  • 17 Correlation of indegree and outdegree over 50% of nodes have indegree within 20% of their outdegree
  • Slide 18
  • 18 Path lengths and diameter all four networks have short path length
  • Slide 19
  • 19 Link degree correlations JDD: joint degree distribution(how often nodes of different degree connect to each other) K nn --- mapping between outdegree and average indegree of all nodes connected to nodes of that outdegree Used for aproxmation of JDD YouTube different due to extremely popular users being connected to by many unpopular users Orkut shows bump due to undersampling
  • Slide 20
  • Measurement and Analysis of Online Social Networks 20 Joint degree distribution and Scale-free behaviour undersampling of low-degree nodes celebrity-driven nature cap on links
  • Slide 21
  • Measurement and Analysis of Online Social Networks 21 Densely connected core removing 10% of core nodes results in breaking up graph into millions of very small SCCs graphs below show results as nodes are removed starting with highest- degree nodes (left) and path length as graph is constructed beginning with highest-degree nodes(right) Sub logarithmic growth
  • Slide 22
  • Measurement and Analysis of Online Social Networks 22 Tightly clustered fringe based on clustering coefficient social network graphs show stronger clustering, most likely due to mutual friends Possibly because personal content is not shared
  • Slide 23
  • Measurement and Analysis of Online Social Networks 23 Groups group sizes follow power-law distribution represent tightly clustered communities
  • Slide 24
  • Measurement and Analysis of Online Social Networks 24 Groups Orkut special case maybe because of partial crawl
  • Slide 25
  • Measurement and Analysis of Online Social Networks 25 Node Value Determination Directed Graph, current model nodes with many incoming links (hubs) have value due to their connection to many users it becomes easy to spread important information to the other nodes, e.g. DNS unhealthy in case of spam or viruses in order for a user to send spam, they have become a more important node, amass friends
  • Slide 26
  • Questions?

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