Planetary-scale views on a large instant-messaging network

By Jure Leskovec, Eric Horvitz. The Web Conference, 2008

651 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fdeb7b7c1f1482073f64a546838edf0f4fdb2397

How researchers see this paper

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

A Critical Review of Centrality Measures in Social Networks (2010, background): “This so-called " small world phenomenon " , which is also known under the heading " six degrees of separation " , can be observed both in the offline and in the online world (e.g., Dodds et al. 2003; Leskovec and Horvitz 2008; Travers and Milgram 1969).”
Yes, there is a correlation: - from social networks to personal behavior on the web (2008, methodology): “Leskovec and Horvitz [14] also performed an analysis on messenger data which overlaps with the data used in our experime nts.”
Open challenges for data stream mining research (2014, background): “Indeed, people are more likely to talk to the opposite sex [13].”
On the Convexity of Latent Social Network Inference (2010, background): “The emergence of large scale social computing applications has made massi ve ocial network data [16] available, but there are important settings where network data is hard t o ob ain and thus the whole network must thus be inferred from the data.”
Do the Young Live in a "Smaller World" Than the Old? Age-Specific Degrees of Separation in a Large-Scale Mobile Communication Network (2016, methodology): “In this work, rather than employing the sampling and probabilistic methods used in previous work [4, 23], we instead leverage a large computing cluster to determine the average shortest paths between all pairs of users, that is n× (n− 1)/2 pairs, where n is the number of users in each network.”
Information network or social network?: the structure of the twitter follow graph (2014, background): “This is interesting in that the ﬁgure is much lower compared to other social networks— in both the Facebook graph [14] and the MSN messenger graph [8], the largest connected component contains more than 99% of all vertices.”
Friendship and mobility: user movement in location-based social networks (2011, background): “Studies have also explored how social networks are embed into the underlying geography [22 , 19, 29], and how geo-data can be used to infer social ties [5, 8, 6] .”
Socio-Spatial Properties of Online Location-Based Social Networks (2011, background): “These properties confirm the smallworld nature of LBSNs, as found in other online social systems (Leskovec and Horvitz 2008).”
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks (2009, background): “Such studies document the persistent structural properties of networks (4), how they form, evolve, and dissolve (5), and how their structure is correlated with social interaction (1, 6, 7), individual and collaborative team performance (8–11), health outcomes (12–14), and global product demand patte”
On the evolution of user interaction in Facebook (2009, background): “[12] analyzed an activity network based on user interaction in a large instant messaging network.”
Community Detection and Mining in Social Media (2010, background): “6 (Leskovec and Horvitz, 2008).”
The Anatomy of the Facebook Social Graph (2011, background): “Two recent studies of Renren [15] and MSN messenger [14] included 42 million and 180 million individuals respectively.”