NDLI: Intra graph clustering using collaborative similarity measure

Content Provider	Springer Nature Link
Author	Nawaz, Waqas Khan, Kifayat Ullah Lee, Young Koo Lee, Sungyoung
Copyright Year	2015
Abstract	Graph is an extremely versatile data structure in terms of its expressiveness and flexibility to model a range of real life phenomenon. Various networks like social networks, sensor networks and computer networks are represented and stored in the form of graphs. The analysis of these kind of graphs has an immense importance from quite a long time. It is performed from various aspects to get maximum out of such multifaceted information repository. When the analysis is targeted towards finding groups of vertices based on their similarity in a graph, clustering is the most conspicuous option. Previous graph clustering approaches either focus on the topological structure or attributes likeness, however, few recent methods constitutes both aspects simultaneously. Due to enormous computation requirements for similarity estimation, these methods are often suffered from scalability issues. In order to overcome this limitation, we introduce collaborative similarity measure (CSM) for intra-graph clustering. CSM is based on shortest path strategy, instead of all paths, to define structural and semantic relevance among vertices. First, we calculate the pair-wise similarity among vertices using CSM. Second, vertices are grouped together based on calculated similarity under k-Medoid framework. Empirical analysis, based on density, and entropy, proves the efficacy of CSM over existing measures. Moreover, CSM becomes a potential candidate for medium scaled graph analysis due to an order of magnitude less computations.
Starting Page	583
Ending Page	603
Page Count	21
File Format	PDF
ISSN	09268782
Journal	Distributed and Parallel Databases
Volume Number	33
Issue Number	4
e-ISSN	15737578
Language	English
Publisher	Springer US
Publisher Date	2015-01-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Graph clustering Collaborative similarity k-Medoid clustering Entropy Density Jaccard similarity coefficient Database Management Data Structures Information Systems Applications (incl. Internet) Operating Systems Memory Structures
Content Type	Text
Resource Type	Article
Subject	Information Systems and Management Information Systems Software Hardware and Architecture

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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