Collaborative Multi-View Attributed Networks Mining

Abstract

Graph clustering techniques are very useful for detecting densely connected groups in large graphs. Many existing graph clustering methods mainly focus on the topological structure, but ignore the vertex properties. Existing graph clustering methods have been recently extended to deal with nodes attribute. In this paper we propose a new method which uses the nodes attributes information along with the topological structure of the network in the clustering process. In order to use the information about the attributes nodes, the collaborative clustering can be employed in the model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple sites by applying different clustering algorithms and therefore improve the final clustering result. The purpose of this article is to introduce a new attributed collaborative multi-view networks based on community detection in networks and topological collaborative learning. The idea consists in modifying databases by adding virtual points which convey clustering information, to change the position of centers of the clustering solution. Experimental results demonstrate the effectiveness of the proposed method through comparisons with the state-of-the-art graph clustering methods on synthetic and real datasets.