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Automatic detection of cohesive subgroups within social hypertext: A heuristic approach
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A measurement-driven analysis of information propagation in the flickr social network
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Connections between the lines: augmenting social networks with text
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TANGENT: a novel, 'Surprise me', recommendation algorithm
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Scalable graph clustering using stochastic flows: applications to community discovery
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Sybil-resilient online content voting
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An event-based framework for characterizing the evolutionary behavior of interaction graphs
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Mining communities in networks: a solution for consistency and its evaluation
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Kronecker Graphs: An Approach to Modeling Networks
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Empirical comparison of algorithms for network community detection
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Towards understanding the external links of video sharing sites: measurement and analysis
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Almost tight bounds for rumour spreading with conductance
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A measurement study of external links of YouTube
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Revealing social networks of spammers through spectral clustering
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Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
The community-search problem and how to plan a successful cocktail party
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Markov clustering of protein interaction networks with improved balance and scalability
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Designs to account for trust in social network-based sybil defenses
Proceedings of the 17th ACM conference on Computer and communications security
Expansion and search in networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Rumour spreading and graph conductance
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Clustering method incorporating network topology and dynamics
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
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IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
The impact of unlinkability on adversarial community detection: effects and countermeasures
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Overlapping communities in dynamic networks: their detection and mobile applications
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
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ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Mathematical continuity in dynamic social networks
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Inferring Networks of Diffusion and Influence
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SBV-Cut: Vertex-cut based graph partitioning using structural balance vertices
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Clustered embedding of massive social networks
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A large body of work has been devoted to identifying community structure in networks. A community is often though of as a set of nodes that has more connections between its members than to the remainder of the network. In this paper, we characterize as a function of size the statistical and structural properties of such sets of nodes. We define the network community profile plot, which characterizes the "best" possible community - according to the conductance measure - over a wide range of size scales, and we study over 70 large sparse real-world networks taken from a wide range of application domains. Our results suggest a significantly more refined picture of community structure in large real-world networks than has been appreciated previously. Our most striking finding is that in nearly every network dataset we examined, we observe tight but almost trivial communities at very small scales, and at larger size scales, the best possible communities gradually "blend in" with the rest of the network and thus become less "community-like." This behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, this behavior is exactly the opposite of what one would expect based on experience with and intuition from expander graphs, from graphs that are well-embeddable in a low-dimensional structure, and from small social networks that have served as testbeds of community detection algorithms. We have found, however, that a generative model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community structure similar to our observations.