Statistical properties of community structure in large social and information networks

By Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney. 2008

866 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Inferring networks of diffusion and influence (2010, neutral): “962]) and a core-periphery network [22] ([0.”
Reconstruction and estimation in the planted partition model (2012, background): “[24] collected and studied a vast collection of large network datasets, ranging from social networks like LinkedIn and MSN Messenger, to collaboration networks in movies and on the arXiv, to biological networks in yeast.”
Graph Clustering with Graph Neural Networks (2020, background): “We attribute that to extremely uneven structure of these graphs, as popular products are co-purchased with a lot of other items, so the effects discussed in [17, 32] are prohibiting good cuts.”
Leveraging social media networks for classification (2011, background): “Leskovec [ 23 ] studied the statistical properties of communities on the giant connected componentandfoundasimilarpattern.Theoptimalspectralcutalwaysreturnsacommunityof100to200nodes,looselyconnected(say,oneortwoedges)totheremaining network.”
EmptyHeaded: A Relational Engine for Graph Processing (2015, background): “23 Tweets about Higgs Boson LiveJournal [23] 4.”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, result): “In addition, this paper is substantially the same as our manuscript of March 7, 2008 [114].”
An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, background): “In later work, Leskovec et al. [2008] conducted an extensive analysis of community structure on 70 social and information networks.”
A state-space mixed membership blockmodel for dynamic network tomography (2008, background): “…formally characterized in the networks perspective using measures of structural cohesiveness and embeddedness [Moody and White (2003)], detecting outbreaks [Leskovec et al. (2007)], and characterizing macroscopic properties of various large social and information networks [Leskovec et al. (2008)].”
LASAGNE: Locality and Structure Aware Graph Node Embedding (2017, methodology): “Alternatively, realistic graphs typically have large-scale properties that are very poorly structured with respect to the behavior of random walks [20], [22], [21], [15], [2], [3].”
Uncovering the Temporal Dynamics of Diffusion Networks (2011, neutral): “9] ) and core-periphery (Leskovec et al., 2008) ( [0 .”
FENNEL: streaming graph partitioning for massive scale graphs (2014, background): “The notion of a community has been formalized in various ways, see [40].”
The tube over time: characterizing popularity growth of youtube videos (2011, methodology): “Categories and Subject Descriptors C.4 [Computer Systems Organization]: Performance of Systems Measurement techniques; H.3.5 [Information Storage and Retrieval]: Online Information Services Web-based services General Terms Human Factors, Measurement Permission to make digital or hard copies of…”
Clustering and Community Detection in Directed Networks: A Survey (2013, background): “[149, 150] observed that in large scale social and information networks, “good” communities exist only at very small scales (of about 100 nodes), while at larger scales the communities gradually blend in with the rest of the network.”
Potential networks, contagious communities, and understanding social network structure (2013, background): “This model provides an alternate view of community structure compared to that metrics such as modularity [29] and conductance [24].”
Community Detection in General Stochastic Block models: Fundamental Limits and Efficient Algorithms for Recovery (2015, background): “Note that these regimes are not only rich mathematically, but are also relevant for applications, as a vast collection of real networks ranging from social (LinkedIn, MSN), collaborative (movies, arXiv), or biological (yeast) networks and more were shown to be sparse [42], [43].”
Empirical comparison of algorithms for network community detection (2010, methodology): “community profile (NCP) [27, 26] that characterizes the quality of”