Group formation in large social networks: membership, growth, and evolution

By Lars Backstrom, Dan Huttenlocher, Jon M. Kleinberg, Xiangyang Lan. Knowledge Discovery and Data Mining, 2006

2,119 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/09031aa6d6743bebebc695955cd77c032cd9192f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Fast shortest path distance estimation in large networks (2009, background): “More recently, with the increasing availability of very large graphs, computer scientists have started studying community detection in the web [18, 32], and in social networks [4, 26, 31].”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “• LiveJournal12 [Backstrom et al. 06]: LiveJournal is an online blogging site where users can create friendship links to other users.”
Kronecker Graphs: An Approach to Modeling Networks (2008, neutral): “…of ArXivhep-ph papers (Gehrke et al., 2003) Collaboration networks CA-DBLP 425,957 2,696,489 317,080 0.74 0.73 23 6.75 DBLP co-authorship network (Backstrom et al., 2006) CA-GR-QC 5,242 28,980 4,158 0.79 0.66 17 6.1 Co-authorship network ingr-qc category of ArXiv (Leskovec et al., 2005b)…”
Topic modeling with network regularization (2008, background): “On the other hand, social network analysis (SNA) focuses on the topology structure of a network [11, 13, 1, 12], addressing questions such as “what the diameter of a network is [13]”, “how a network evolves [13, 1]”, “how information diffuses on the network [9, 14]”, and “what are the communities on”
DeepInf: Social Influence Prediction with Deep Learning (2018, background): “Centrality [6]. Clustering Coecient [50]. Rarity (reciprocal of ego user's degree) [1]. Embedding Pre-trained network embedding (DeepWalk [36], 64-dim). Ego The number/ratio of active neighbors [2]. Density of subgnetwork induced by active neighbors [46]. #Connected components formed by active ”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “Vertices Edges LiveJournal [9] 4.8M 69M Twitter 2010 [31] 42M 1.5B UK web graph 2007 [10] 109M 3.7B Yahoo web [8] 1.4B 6.6B Many specialized graph processing frameworks (e.g. [20, 21, 32, 44]) have been developed to run on web and social graphs such as those shown in Table 1.”
A classification for community discovery methods in complex networks (2011, methodology): “[139] can be used as a framework after a community detection algorithm in order to obtain a temporal evolving description of the identified groups.”
Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter (2011, background): “…ABSTRACT There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been dif.cult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment.”
Optimal marketing strategies over social networks (2008, methodology): “Studies such as [1] can be used to determine the precise form of the functions fi.”
Social ties and their relevance to churn in mobile telecom networks (2008, methodology): “A particular area of interest has been to study how online communities evolve over time [2,18].”
Overlapping community detection at scale: a nonnegative matrix factorization approach (2013, background): “communities) are often interpreted as organizational units in social networks [7, 29], functional units in biochemical networks [17], ecological niches in food web networks [10], or scientific disciplines in citation and collaboration networks [3].”
Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography (2007, background): “…Research Dept. of Computer Science Cornell University, Ithaca NY Cornell University, Ithaca NY dwork@microsoft.com lars@cs.cornell.edu kleinber@cs.cornell.edu ABSTRACT In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them.”
Modeling relationship strength in online social networks (2010, background): “[3] investigated the evolution of network structure and group membership in MySpace and LiveJournal and showed that homophily can be used to improve predictive models of group membership.”
Tracing information flow on a global scale using Internet chain-letter data (2008, result): “Previous research has shown this network to have characteristics consistent with other large-scale social networks, and the organization of the online links is analogous to the e-mail contact lists that were used to propagate the chain letter (28, 29).”
Time-varying graphs and dynamic networks (2010, methodology): “The formalism introduced therein to represent dynamic graphs has been used as a framework for several works such as [6,27,47,65].”
Statistical properties of community structure in large social and information networks (2008, background): “LiveJournal 4,843,953 42,845,684 Blog friendships [5] Epinions 75,877 405,739 Trust network [28] CA-DBLP 317,080 1,049,866 Co-authorship [5] • Information (citation) networks”