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A Novel Graph Clustering Algorithm Based on Structural Attribute Neighborhood Similarity (SANS)
Published in Springer India
Volume: 43
Pages: 467 - 474
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. © Springer India 2016.
About the journal
JournalData powered by TypesetProceedings of 3rd International Conference on Advanced Computing, Networking and Informatics Smart Innovation, Systems and Technologies
PublisherData powered by TypesetSpringer India
Open Access0