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A Novel Graph Clustering Algorithm Based on Structural Attribute Neighborhood Similarity (SANS)

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Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 43))

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Abstract

Graph Clustering techniques are widely used in detecting densely connected graphs from a graph network. Traditional Algorithms focus only on topological structure but mostly ignore heterogeneous vertex properties. In this paper we propose a novel graph clustering algorithm, Structural Attribute Neighbourhood Similarity (SANS) algorithm, provides an efficient trade-off between both topological and attribute similarities. First, the algorithm partitions the graph based on structural similarity, secondly the degree of contribution of vertex attributes with the vertex in the partition is evaluated and clustered. An extensive experimental result proves the effectiveness of SANS cluster with the other conventional algorithms.

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Authors and Affiliations

  1. School of Information Technology & Engineering, VIT University, Vellore, 632014, India

    M. Parimala & Daphne Lopez

Authors
  1. M. Parimala
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  2. Daphne Lopez
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Corresponding author

Correspondence to M. Parimala .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  2. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Durga Prasad Mohapatra

  3. Computer Science & Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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Parimala, M., Lopez, D. (2016). A Novel Graph Clustering Algorithm Based on Structural Attribute Neighborhood Similarity (SANS). In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_48

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  • DOI: https://doi.org/10.1007/978-81-322-2538-6_48

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2537-9

  • Online ISBN: 978-81-322-2538-6

  • eBook Packages: EngineeringEngineering (R0)

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