Measurement and analysis of online social networks

By Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, Bobby Bhattacharjee. ACM/SIGCOMM Internet Measurement Conference, 2007

3,406 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7631c91b69c6ec58a352bf7c3121282770fdbe20

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In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Identifying Platform Effects in Social Media Data (2016, background): “Multiple works have considered whether network metrics of large online social networks differ from those of previously studied social networks (Corten 2012; Quercia, Capra, and Crowcroft 2012; Ugander et al. 2011; Mislove et al. 2007); we can continue to theorize how differences result from platform”
Routing in Socially Selfish Delay Tolerant Networks (2010, methodology): “76 [25] when generating node degrees, but change the minimum acceptable degree.”
Fast exact shortest-path distance queries on large networks by pruned landmark labeling (2013, neutral): “com) [26].”
Streaming graph partitioning for large distributed graphs (2012, methodology): “5.3 Performance on all graphs: discussion We present the gain in the performance of each heuristic in Table 2, averaged over all datasets from Table 1 (except for LiveJournal and Twitter) and all runs, for each ordering.”
Advances in Social Networks Analysis and Mining ( ASONAM ) DE-Crawler : A Densification-Expansion Algorithm for Online Data Collection (2018, background): “4) Breadth-first Search (BFS): Due to its simplicity, BFS is one of the most popular crawling algorithms [9].”
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, background): “Note that social networks are often sparse graphs [25], [26].”
Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands (2011, background): “Despite the popularity of SNS, their importance in shaping commercial online interaction (Mislove et al. 2007) and their potential to support brands (Christodoulides 2009), research into SNS is very limited, and focuses largely on the consumer in a B2C domain.”
Detecting cohesive and 2-mode communities indirected and undirected networks (2014, background): “Youtube differs from other social networks in one important way: Edges in Youtube are essentially “subscriptions” for content rather than mutual friendships; consequently, high degree nodes tend to connect to low degree nodes [33].”
Computing Personalized PageRank Quickly by Exploiting Graph Structures (2014, background): “…preprocessing for a large network (e.g., once a 12]. web-BerkStan web-Google, web-NotreDame, and web-Stanford data sets are available at http://snap.stanford. edu/data/index.html [3, 33]. livejournal, ﬂickr, and orkut data sets are available at http://socialnetworks. mpi-sws.org/datasets.html [37].”
Local search of communities in large graphs (2014, neutral): “Youtube [11] is a user-to-user link network.”
A few chirps about twitter (2008, background): “Although this is somewhat similar to Figure 2(b) in [15] (LiveJournal indegree and outdegree graph), we prefer to make comparisons within our dataset as we understand the Twitter milieu better and we want to stray from the conventional power law result.”
Statistics and Social Network of YouTube Videos (2008, background): “Mislove et al. studied four online social networking sites (Flickr, YouTube, LiveJournal and Orkut) [24], and confirmed the power-law, small-world and scale-free properties of online social networks.”
A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C (2019, neutral): “96 [36] Ecommerce* B gender 15,000 21,880 21.”
Evolution of social-attribute networks: measurements, modeling, and implications using google+ (2012, background): “Furthermore, since we have a unique opportunity to observe the network as it grew, we also analyze how these properties changed as the Google+ SAN evolved.”
The social distributional hypothesis: a pragmatic proxy for homophily in online social networks (2014, neutral): “In Mislove et al. (2007), the contact network in Flickr is analyzed and compared with networks derived from other popular online social networking sites.”
SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks (2010, background): “In fact, social networks are well-known [3, 15, 23, 37] to have groups”
Measuring user activity on an online location-based social network (2011, background): “User activity on online social networks has already been under study [5], [6].”
Bootstrapped Graph Diffusions (2018, methodology): “The data sets include three citation networks: Cora, Pubmed, and Citeseer from [36], one Knowledge graph (entity classification) dataset (NELL) preprocessed by [40] from [7], and the YouTube group membership data set from [28].”
FAST-PPR: scaling personalized pagerank estimation for large graphs (2014, methodology): “The LiveJournal, Orkut (social networks) and DBLP (collaborations on papers) networks are all undirected – for each, we have the largest connected component of the overall graph.”