Structure and evolution of online social networks

By Ravi Kumar, Jasmine Novak, Andrew Tomkins. Knowledge Discovery and Data Mining, 2006

1,599 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f5d425bfc32a68e21305bb832a5a8e87f9f83264

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

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].”
Sensing Trending Topics in Twitter (2013, background): “evolution of the online worlds [5]–[9], is now increasingly”
Microscopic evolution of social networks (2008, background): “Similarly, macroscopic properties of network evolution, like densification and shrinking diameters, were examined [16, 11, 19, 13].”
Social Network Sites: Definition, History, and Scholarship (2007, neutral): “the growth of Flickr and Yahoo! 360's networks, Kumar, Novak, and Tomkins (2006) argued that there are”
Community detection in graphs (2009, background): “Recently, several data sets have become available, enabling to monitor the evolution in time of real systems (Kumar et al., 2003, 2006; Leskovec et al., 2008, 2005).”
K-Automorphism: A General Framework For Privacy Preserving Network Publication (2009, methodology): “As a consequence, the amount of social network data has grown rapidly, and this offers rich opportunities for data mining and analysis, for example, to ﬁnd community groups and their evolution [2, 10].”
The Matthew effect in empirical data (2014, background): “Online social networks, such as the internet encyclopedia Wikipedia [74], bulletin board systems [76], social networking services like Flickr, the obsolete Yahoo! 360◦ or the now popular Facebook [73, 77], as well as longitudinal microblogging data [78] also show evidence of the Matthew effect.”
Comorbidity: A network perspective (2010, background): “Taking all results together, it is likely that overlapping symptoms play a more important role in explaining comorbidity than was originally thought.”
A new space for comparing graphs (2014, background): “Thus, we can expect the collaboration ego network of an individual to contain information about the characteris ic of his/her research.”
Wherefore Art Thou R3579x? Anonymized Social Networks, Hidden Patterns, and Structural Steganography (background): “…most promising opportunities for the study of social networks are emerging from data on domains where users have strong expectations of privacy — these include e-mail and messaging networks, as well as the link structure of closed (i.e. " members-only " ) on-line communities [1] [2] [17] [19] [21].”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “42 Flickr photo sharing social network [101] LinkedIn 6,946,668 30,507,070 0.”
Measurement and analysis of online social networks (2007, background): “[26] examine two online social networks and find that both possess a large strongly connected component.”
K-isomorphism: privacy preserving network publication against structural attacks (2010, background): “Kumar, Novak, Tomkins studied the structure and evolution of the network [16].”
Graph Kernels (2008, neutral): “…learning in domains such as bioinformatics (Sharan and Ideker, 2006), chemoinformatics (Bonchev and Rouvray, 1991), drug discovery (Kubinyi, 2003), web data mining (Washio and Motoda, 2003), and social networks (Kumar et al., 2006) involves the study of relationships between structured objects.”
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (2008, background): “More recently, there has been a growing body of work on the analysis of communities and their temporal evolution in dynamic networks [1, 8, 9, 10, 11, 16, 19, 21].”
Understanding business ecosystem using a 6C framework in Internet-of-Things-based sectors (2015, background): “29 ecosystem is very fragmented; it is a very dynamic loose network or social network (Iansiti & Levien, 2004; Kumar et al., 2010; Rong et al., 2013).”
No country for old members: user lifecycle and linguistic change in online communities (2013, background): “For example, previous research has examined the dynamics of online social networks, studying the evolution of the whole network structure [21, 28], of groups inside these networks [2, 10, 16, 50], as well as the social tie creation between individual users [29].”