Results 1  10
of
111
The structure and function of complex networks
 SIAM REVIEW
, 2003
"... Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, ..."
Abstract

Cited by 1407 (9 self)
 Add to MetaCart
Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the smallworld effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Finding community structure in networks using the eigenvectors of matrices. Phys
 Rev. E
"... We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible div ..."
Abstract

Cited by 224 (0 self)
 Add to MetaCart
We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of realworld complex networks. I.
Comparing community structure identification
 Journal of Statistical Mechanics: Theory and Experiment
, 2005
"... ..."
Statistical properties of community structure in large social and information networks
"... 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 structur ..."
Abstract

Cited by 120 (10 self)
 Add to MetaCart
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 realworld networks taken from a wide range of application domains. Our results suggest a significantly more refined picture of community structure in large realworld 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 “communitylike.” This behavior is not explained, even at a qualitative level, by any of the commonlyused 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 wellembeddable in a lowdimensional 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.
Computing communities in large networks using random walks
 J. of Graph Alg. and App. bf
, 2004
"... Dense subgraphs of sparse graphs (communities), which appear in most realworld complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advan ..."
Abstract

Cited by 94 (2 self)
 Add to MetaCart
Dense subgraphs of sparse graphs (communities), which appear in most realworld complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advantages: it captures well the community structure in a network, it can be computed efficiently, and it can be used in an agglomerative algorithm to compute efficiently the community structure of a network. We propose such an algorithm, called Walktrap, which runs in time O(mn 2) and space O(n 2) in the worst case, and in time O(n 2 log n) and space O(n 2) in most realworld cases (n and m are respectively the number of vertices and edges in the input graph). Extensive comparison tests show that our algorithm surpasses previously proposed ones concerning the quality of the obtained community structures and that it stands among the best ones concerning the running time.
Community structure in large networks: Natural cluster sizes and the absence of large welldefined clusters
, 2008
"... 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 wit ..."
Abstract

Cited by 79 (6 self)
 Add to MetaCart
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 realworld networks, ranging from traditional and online social networks, to technological and information networks and
REMINDIN': Semantic Query Routing in PeertoPeer Networks Based on Social Metaphors
, 2004
"... In peertopeer networks, finding the appropriate answer for an information request, such as the answer to a query for RDF(S) data, depends on selecting the right peer in the network. We here investigate how social metaphors can be exploited effectively and efficiently to solve this task. To this en ..."
Abstract

Cited by 72 (12 self)
 Add to MetaCart
In peertopeer networks, finding the appropriate answer for an information request, such as the answer to a query for RDF(S) data, depends on selecting the right peer in the network. We here investigate how social metaphors can be exploited effectively and efficiently to solve this task. To this end, we define a method for query routing, REMINDIN', that lets (i) peers observe which queries are successfully answered by other peers, (ii), memorizes this observation, and, (iii), subsequently uses this information in order to select peers to forward requests to.
A framework for community identification in dynamic social networks
 In KDD
, 2007
"... We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as “unusually densely knit ” subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating ..."
Abstract

Cited by 60 (5 self)
 Add to MetaCart
We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as “unusually densely knit ” subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Instead, we propose an optimizationbased approach for modeling dynamic community structure. We prove that finding the most explanatory community structure is NPhard and APXhard, and propose algorithms based on dynamic programming, exhaustive search, maximum matching, and greedy heuristics. We demonstrate empirically that the heuristics trace developments of community structure accurately for several synthetic and realworld examples.
Centerpiece subgraphs: Problem definition and fast solutions
 In KDD
, 2006
"... Given Q nodes in a social network (say, authorship network), how can we find the node/author that is the centerpiece, and has direct or indirect connections to all, or most of them? For example, this node could be the common advisor, or someone who started the research area that the Q nodes belong t ..."
Abstract

Cited by 52 (17 self)
 Add to MetaCart
Given Q nodes in a social network (say, authorship network), how can we find the node/author that is the centerpiece, and has direct or indirect connections to all, or most of them? For example, this node could be the common advisor, or someone who started the research area that the Q nodes belong to. Isomorphic scenarios appear in law enforcement (find the mastermind criminal, connected to all current suspects), gene regulatory networks (find the protein that participates in pathways with all or most of the given Q proteins), viral marketing and many more. Connection subgraphs is an important first step, handling the case of Q=2 query nodes. Then, the connection subgraph algorithm finds the b intermediate nodes, that provide a good connection between the two original query nodes. Here we generalize the challenge in multiple dimensions: First, we allow more than two query nodes. Second, we allow a whole family of queries, ranging from ’OR ’ to ’AND’, with ’softAND ’ inbetween. Finally, we design and compare a fast approximation, and study the quality/speed tradeoff. We also present experiments on the DBLP dataset. The experiments confirm that our proposed method naturally deals with multisource queries and that the resulting subgraphs agree with our intuition. Wallclock timing results on the DBLP dataset show that our proposed approximation achieve good accuracy for about 6: 1 speedup. This material is based upon work supported by the
Semisupervised learning on directed graphs
 In NIPS
, 2005
"... Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions defined over nodes of a directed graph ..."
Abstract

Cited by 49 (2 self)
 Add to MetaCart
Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions defined over nodes of a directed graph that forces the classification function to change slowly on densely linked subgraphs. A powerful, yet computationally simple classification algorithm is derived within the proposed framework. The experimental evaluation on realworld Web classification problems demonstrates encouraging results that validate our approach. 1