Local method for detecting communities

Bagrow, James P.; Bollt, Erik M.

doi:10.1103/physreve.72.046108

Cited by 258 publications

(189 citation statements)

References 17 publications

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“…In light of our results, such methods seem promising more generally. Other recent work that has focused on developing local and/or near-linear time heuristics for community detection include [49,158,47,21,137].…”

Section: Relationship With Community Identification Methodsmentioning

confidence: 99%

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

Leskovec¹,

Lang²,

Dasgupta³

et al. 2009

Internet Mathematics

1,512

1,082

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A large body of work has been devoted to defining and identifying clusters or communities in social and information networks, i.e., in graphs in which the nodes represent underlying social entities and the edges represent some sort of interaction between pairs of nodes. Most such research begins with the premise that a community or a cluster should be thought of as a set of nodes that has more and/or better connections between its members than to the remainder of the network. In this paper, we explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions.Rather than defining a procedure to extract sets of nodes from a graph and then attempt to interpret these sets as a "real" communities, we employ approximation algorithms for the graph partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, 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. We study over 100 large real-world networks, ranging from traditional and on-line social networks, to technological and information networks and web graphs, and ranging in size from thousands up to tens of millions of nodes.Our results suggest a significantly more refined picture of community structure in large networks than has been appreciated previously. Our observations agree with previous work on small networks, but we show that large networks have a very different structure. In particular, we observe tight communities that are barely connected to the rest of the network at very small size scales (up to ≈ 100 nodes); and communities of size scale beyond ≈ 100 nodes gradually "blend into" the expanderlike core of the network and thus become less "community-like," with a roughly inverse relationship between community size and optimal community quality. This observation agrees well with the so-called Dunbar number which gives a limit to the size of a well-functioning community.However, this behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, it is exactly the opposite of what one would expect based on intuition from expander graphs, low-dimensional or manifold-like graphs, and from small social networks that have served as testbeds of community detection algorithms. The relatively gradual increase of the network community profile plot as a function of increasing community size depends in a subtle manner on the way in which local clustering information is propagated from smaller to larger size scales in the network. We have found that a generative graph model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community profile plot similar to what we observe i...

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Section: Relationship With Community Identification Methodsmentioning

confidence: 99%

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

Leskovec¹,

Lang²,

Dasgupta³

et al. 2009

Internet Mathematics

1,512

1,082

View full text Add to dashboard Cite

show abstract

“…Label flooding algorithms have also been used in detecting communities in networks [27,28]. In [27], the authors propose a local community detection method where a node is initialized with a label which then propagates step by step via the neighbors until it reaches the end of the community, where the number of edges proceeding outward from the community drops below a threshold value.…”

Section: Definitions and Previous Workmentioning

confidence: 99%

“…In [27], the authors propose a local community detection method where a node is initialized with a label which then propagates step by step via the neighbors until it reaches the end of the community, where the number of edges proceeding outward from the community drops below a threshold value. After finding the local communities at all nodes in the network, an n × n matrix is formed, where the ij th entry is 1 if node j belongs to the community started from i and 0 otherwise.…”

Section: Definitions and Previous Workmentioning

confidence: 99%

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Near linear time algorithm to detect community structures in large-scale networks

2007

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Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a pre-defined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.

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“…[11,12,13,17,18,19,20,21,22,23,24,28,26,27]), and hierarchical extensions of the node degree and clustering coefficient were only more recently formalized in [9,10] by using concepts derived from mathematical morphology [25,29,30] including dilations and distance transforms in graphs. Despite their recent introduction, such concepts have already yielded valuable results when applied to essentiality of protein-protein interaction networks [37], bone structure characterization [38], and community finding [32,33].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Characterization of Complex Networks

Costa

Silva

2006

J Stat Phys

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While the majority of approaches to the characterization of complex networks has relied on measurements considering only the immediate neighborhood of each network node, valuable information about the network topological properties can be obtained by considering further neighborhoods. The current work discusses on how the concepts of hierarchical node degree and hierarchical clustering coefficient (introduced in cond-mat/0408076), complemented by new hierarchical measurements, can be used in order to obtain a powerful set of topological features of complex networks. The interpretation of such measurements is discussed, including an analytical study of the hierarchical node degree for random networks, and the potential of the suggested measurements for the characterization of complex networks is illustrated with respect to simulations of random, scale-free and regular network models as well as real data (airports, proteins and word associations). The enhanced characterization of the connectivity provided by the set of hierarchical measurements also allows the use of agglomerative clustering methods in order to obtain taxonomies of relationships between nodes in a network, a possibility which is also illustrated in the current article.

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Local method for detecting communities

Cited by 258 publications

References 17 publications

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

Near linear time algorithm to detect community structures in large-scale networks

Hierarchical Characterization of Complex Networks

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