gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
GREW-A Scalable Frequent Subgraph Discovery Algorithm
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Bridging centrality: graph mining from element level to group level
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking of Closeness Centrality for Large-Scale Social Networks
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Fast centrality approximation in modular networks
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Topological tree clustering of social network search results
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Structural correlation pattern mining for large graphs
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
A better strategy of discovering link-pattern based communities by classical clustering methods
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Social Networks are gaining importance due to their enablement of modeling various types of interactions among individuals, communities and organizations. Network Topologies play a major role in analyzing the social networks for a variety of business application scenarios such as finding influencers in product campaigning and virtual communities to recommend music downloads. Social networks are dynamic in nature and detection of topologies from these networks presents a host of new challenges. In this paper, we present approaches for topology discovery, particularly star, ring and mesh, based on the measures of network centrality. These approaches facilitate an efficient way of discovering topologies for analyzing large social networks. We also discuss experiments on DBLP dataset to show the viability of our proposed approach.