Self-Organization and Identification of Web Communities

By Gary William Flake, Steve Lawrence, C. Lee Giles, Frans Coetzee. Computer, 2002

1,048 citations recorded by the source corpus.

The vast improvement in information access is not the only advantage resulting from the increasing percentage of hyperlinked human knowledge available on the Web. Additionally, much potential exists for analyzing interests and relationships within science and society. However, the Web's decentralized and unorganized nature hampers content analysis. Millions of individuals operating independently and having a variety of backgrounds, knowledge, goals and cultures author the information on the Web. Despite the Web's decentralized, unorganized, and heterogeneous nature, our work shows that the Web self-organizes and its link structure allows efficient identification of communities. This self-organization is significant because no central authority or process governs the formation and structure of hyperlinks.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8c795cc8a94b1e5f842f8d613e66297a1c1c9efe

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In Statistical and Nonlinear Physics

Citations in the corpus

Finding Statistically Significant Communities in Networks (2010, neutral): “subject [11], compartments in food webs [12,13], functional modules in protein interaction networks [14], biochemical pathways in metabolic networks [15,16], etc.”
The community-search problem and how to plan a successful cocktail party (2010, methodology): “Discovering communities in graphs and social networks has drawn a large amount of attention in recent years [9, 13, 15, 16, 25].”
Statistical properties of community structure in large social and information networks (2008, background): “[66, 67] defined a community as a set of nodes that has more intra-edges than inter-edges.”
A classification for community discovery methods in complex networks (2011, background): “Basically, this definition of the problem overlaps with another well-known data mining problem: influence spread and flow maximization [1], which is often used for viral marketing [132].”
Measuring search engine bias (2005, neutral): “This is one way of showing the structure of a response set of URLs obtained by one or more search engines for a given query ( Flake, Lawrence, Giles, & Coetzee, 2002 ).”
Finding and evaluating community structure in networks. (2003, neutral): “[40] and Adamic and Adar [41].”
Community Structure in Graphs (2007, background): “So, communities may correspond to groups of pages of the World Wide Web dealing with related topics [11], to functional modules such as cycles and pathways in metabolic networks [12, 13], to groups of related individuals in social networks [14, 15], to compartments in food webs [16, 17], and so on.”
An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, background): “The problem of identifying these clusters or communities from static graphs has been extensively studied [Girvan and Newman 2002; Clauset et al. 2004; Flake et al. 2002 ] over the past decade.”
Local method for detecting communities. (2004, background): “Community could be loosely described as a collection of vertices within a graph that are densely connected amongst themselves while being loosely connected to the rest of the graph [10, 11 , 12].”
Fast discovery of connection subgraphs (2004, background): “There has been considerable work on community detection [11, 9, 12].”
Analysis of the structure of complex networks at different resolution levels (2007, background): “This is the case, for example in the worldwide airports network [6], the WWW [7], biological networks [8, 9, 10], social networks [3, 11] and the Internet [12, 13], among others.”
Mining communities and their relationships in blogs: A study of online hate groups (2007, background): “Treating the Web as a large graph, the problem of community identification can also be formulated as a minimum-cut problem, which finds clusters of roughly equal sizes while minimizing the number of links between clusters ( Flake et al., 2000, 2002 ).”
Finding community structure in networks using the eigenvectors of matrices. (2006, background): “Social networks [5], biochemical networks [7, 8, 9], and information networks such as the web [10], have all been shown to possess strong community structure, a ﬁnding that has substantial practical implications for our understanding of the systems these networks represent.”
Community detection in graphs (2009, methodology): “[132,25] have recently usedmaximum flows to identify communities in the graph of the World Wide Web.”
Towards the self-annotating web (2004, methodology): “• In [10, 12], the Web structure itself is used to determine a focus for harvesting data.”