Abstract
Information networks contain objects connected by multiple links and described by rich attributes. Detecting community for these networks is a challenging research problem, because there is a scarcity of effective approaches that balance the features of the network structure and the characteristics of the nodes. Some methods detect communities by considering topological structures while ignoring the contributions of attributes. Other methods have considered both topological structure and attributes but pay a high price in time complexity. We establish a new community detection algorithm which explores both topological <u>S</u>tructure and <u>A</u>ttributes using <u>G</u>lobal structure and <u>L</u>ocal neighborhood features (SAGL) which also has low computational complexity. The first step of SAGL evaluates the global importance of every node and calculates the similarity of each node pair by combining edge strength and node attribute similarity. The second step of SAGL uses a clustering algorithm that identifies communities by measuring the similarity of two nodes, calculated by the distance of their neighbors. Experimental results on three real-world datasets show the effectiveness of SAGL, particularly its fast convergence compared to current state-of-the-art attributed graph clustering methods.
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Index Terms
Community Detection with Topological Structure and Attributes in Information Networks
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