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14
Comparing community structure identification
 Journal of Statistical Mechanics: Theory and Experiment
, 2005
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A family of dissimilarity measures between nodes generalizing both the shortestpath and the commutetime distances
 in Proceedings of the 14th SIGKDD International Conference on Knowledge Discovery and Data Mining
"... This work introduces a new family of linkbased dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortestpath (RSP) dissimilarity, depends on a parameter θ and has the interesting property of reducing, on one end, to the standard shortestpath d ..."
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Cited by 25 (11 self)
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This work introduces a new family of linkbased dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortestpath (RSP) dissimilarity, depends on a parameter θ and has the interesting property of reducing, on one end, to the standard shortestpath distance when θ is large and, on the other end, to the commutetime (or resistance) distance when θ is small (near zero). Intuitively, it corresponds to the expected cost incurred by a random walker in order to reach a destination node from a starting node while maintaining a constant entropy (related to θ) spread in the graph. The parameter θ is therefore biasing gradually the simple random walk on the graph towards the shortestpath policy. By adopting a statistical physics approach and computing a sum over all the possible paths (discrete path integral), it is shown that the RSP dissimilarity from every node to a particular node of interest can be computed efficiently by solving two linear systems of n equations, where n is the number of nodes. On the other hand, the dissimilarity between every couple of nodes is obtained by inverting an n × n matrix. The proposed measure can be used for various graph mining tasks such as computing betweenness centrality, finding dense communities, etc, as shown in the experimental section.
Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
 PLoS One
, 2010
"... Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Fin ..."
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Cited by 17 (1 self)
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Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence functionbased, centralitytype community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoomin analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications
Fundamental statistical features and selfsimilar properties of tagged networks
 New Journal of Physics
"... Abstract. We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc.) provide essential information about the entity represented by a given node, thus ..."
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Cited by 9 (0 self)
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Abstract. We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc.) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterise the networks with tagged nodes, we introduce a number of new notions, including tagassortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tagassortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g., the networks we studied are tagassortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pinpointing large scale specific features of realworld complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this selfsimilar property of the networks can be well characterised by the tagassortativity exponent, which is specific to each system. PACS numbers: 02.70.Rr, 05.10.a, 87.16.Yc, 89.20.a, 89.75.HcFundamental statistical features and selfsimilar properties of tagged networks 2 1.
Detecting Community Structure in Complex Networks Using Bacterial Chemotaxis with Fuzzy Cmeans Clustering
, 2014
"... Abstract: Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, the bacterial chemotaxis (BC) strategy is used to maximize the modularity of a network, associating with a dissimilarityindexbased and with a ..."
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Cited by 3 (0 self)
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Abstract: Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, the bacterial chemotaxis (BC) strategy is used to maximize the modularity of a network, associating with a dissimilarityindexbased and with a diffusiondistancebased fuzzy cmeans clustering iterative procedure. The proposed algorithm outperforms most existing methods in the literature as regards the optimal modularity found. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters.
APPROVAL OF THE VIVAVOCE BOARD Certified that the thesis entitled “SelfOrganization of Speech Sound Inventories in the Framework of Complex Networks ” submitted by
, 2009
"... Animesh Mukherjee to the Indian Institute of Technology, Kharagpur, for the award of the degree of Doctor of Philosophy has been accepted by the external examiners and that the student has successfully defended the thesis in the vivavoce examination held today. Members of the DSC ..."
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Animesh Mukherjee to the Indian Institute of Technology, Kharagpur, for the award of the degree of Doctor of Philosophy has been accepted by the external examiners and that the student has successfully defended the thesis in the vivavoce examination held today. Members of the DSC
Under the supervision of
, 2015
"... Methods and Applications ” submitted by Tanmoy Chakraborty to the Indian ..."
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