On the evolution of user interaction in Facebook

By Bimal Viswanath, Alan Mislove, Meeyoung Cha, Krishna P. Gummadi. 2009

1,561 citations recorded by the source corpus.

Online social networks have become extremely popular; numerous sites allow users to interact and share content using social links. Users of these networks often establish hundreds to even thousands of social links with other users. Recently, researchers have suggested examining the activity network - a network that is based on the actual interaction between users, rather than mere friendship - to distinguish between strong and weak links. While initial studies have led to insights on how an activity network is structurally different from the social network itself, a natural and important aspect of the activity network has been disregarded: the fact that over time social links can grow stronger or weaker. In this paper, we study the evolution of activity between users in the Facebook social network to capture this notion. We find that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, we also find that even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “, 2016) or their interaction patterns (Viswanath et al., 2009) are often non-stationary.”
The Anatomy of the Facebook Social Graph (2011, background): “Other studies have analyzed communication patterns and activity amongst segments of the user population [20,21].”
Cultivating Social Resources on Social Network Sites: Facebook Relationship Maintenance Behaviors and Their Role in Social Capital Processes (2014, neutral): “Processes∗”
Predict Anchor Links across Social Networks via an Embedding Approach (2016, methodology): “The ﬁrst dataset 1 was crawled from Facebook and published in [Viswanath et al. , 2009].”
Toward link predictability of complex networks (2015, background): “We consider the following 10 real-world networks drawn from disparate fields: ( i ) Jazz (37), a collaboration network of jazz musicians consists of 198 nodes and 2742 interactions; ( ii ) Metabolic (38), the metabolic network of Caenorhabditis elegans ; ( iii ) Neural (32), the neural network of C.”
SoK: The Evolution of Sybil Defense via Social Networks (2013, methodology): “Indeed, it is quite conceivable that the cut between two tightly-knit communities of honest nodes A and B be as sparse as the cut between A and the sybil region: to an honest node in A using a protocol in the style of SybilLimit, a sybil node would then be indistinguishable from an honest node in B ”
Exploiting Temporal Dynamics in Sybil Defenses (2015, methodology): “For our evaluation, we consider a real-world Facebook interaction graph from the New Orleans regional network [28].”
The structure of online social networks mirrors those in the offline world (2015, methodology): “The semantic description could be the case, for instance, of tagged photos on Flickr and Instagram, or messages annotated with hashtags in Twitter and Facebook.”
The little engine(s) that could: scaling online social networks (2010, methodology): “2008. Facebook: We used a public dataset of the New Orleans Facebook network [ 34 ].”
SybilSCAR: Sybil detection in online social networks via local rule based propagation (2017, methodology): “The Facebook dataset is an interaction graph from the New Orleans regional network [40].”
Habits and the electronic herd: The psychology behind social media’s successes and failures (2020, methodology): “To test discontinuity effects in the ongoing use of social media, we conducted a reanalysis of existing Facebook data on the development of user-to-user interaction, specifically users’ wall postings (for the original data, see Vishwanath et al., 2009 ).”
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, background): “Facebook is an interaction graph from the New Orleans regional network [31].”
Preserving Link Privacy in Social Network Based Systems (2012, methodology): “For the deployment of secure applications that leverage user’s trust relationships, we envision a scenario where these applications bootstrap user’s trust relationships using existing online social networks such as Facebook or Google+.”
A Review of Facebook Research in the Social Sciences (2012, positive): “For example, one study investigating content contributions found that 54% of interactions between pairs of users who interact infrequently were directly attributable to Facebook’s birthday reminder feature (Viswanath et al., 2009).”
Generalized Louvain method for community detection in large networks (2011, background): “Results obtained by analyzing several real-world networks [20], [21] are summarized in Table I.”
An analysis of social network-based Sybil defenses (2010, background): “ity between users may be a better predictor of the strength of the social link between them [30,31].”