Coupled Node Similarity Learning for Community Detection in Attributed Networks

Abstract

Attributed networks consist of not only a network structure but also node attributes. Most existing community detection algorithms only focus on network structures and ignore node attributes, which are also important. Although some algorithms using both node attributes and network structure information have been proposed in recent years, the complex hierarchical coupling relationships within and between attributes, nodes and network structure have not been considered. Such hierarchical couplings are driving factors in community formation. This paper introduces a novel coupled node similarity (CNS) to involve and learn attribute and structure couplings and compute the similarity within and between nodes with categorical attributes in a network. CNS learns and integrates the frequency-based intra-attribute coupled similarity within an attribute, the co-occurrence-based inter-attribute coupled similarity between attributes, and coupled attribute-to-structure similarity based on the homophily property. CNS is then used to generate the weights of edges and transfer a plain graph to a weighted graph. Clustering algorithms detect community structures that are topologically well-connected and semantically coherent on the weighted graphs. Extensive experiments verify the effectiveness of CNS-based community detection algorithms on several data sets by comparing with the state-of-the-art node similarity measures, whether they involve node attribute information and hierarchical interactions, and on various levels of network structure complexity.

Co-Association Matrix-Based Multi-Layer Fusion for Community Detection in Attributed Networks

“…Data sets : in order to test the performance of our method, we selected three networks with node attributes: the counselor relationship network (Consult) [ 15 ], the London gang relationship network (London Gang) [ 55 ] and the Montreal gang relationship network (Montreal Gang) [ 56 ].…”

“…Related work of community detection on attributed networks. The attributed network (or attributed graph) [ 15 , 46 ] is a kind of important complex network, which has both topological structures and node attributes. In the attributed network context, the topological structure represents the interactions between nodes and the attributes describe the inherent characteristics of each node in the network.…”

“…In general, attribute information describes the inherent characteristics of each node and edge and influences the community structure. As the attribute information of node and edge becomes more and more abundant, there are many studies that are beginning to take attribute information into account in the community detection algorithm [ 14 , 15 , 16 , 17 , 18 , 19 ]. However, information fusion is a big challenge [ 20 , 21 ] existing in most community detection algorithms [ 22 ], due to the complexity of multi-source heterogeneous data.…”

Research Frontier in Chaos Theory and Complex Networks

“…By combining the characteristics of network structure with node attributes, Meng et al [11] propose a novel coupled node similarity (CNS) measure to capture both explicit and implicit interactions between nodes in their paper "Coupled node similarity learning for community detection in attributed networks". CNS is used to generate the edge weights and then transfer a plain graph to a weighted graph.…”

A generative model for exploring structure regularities in attributed networks

“…Obviously, using only one type of information will ignore another type of information. It has shown that combing network topology with attribute information can not only improve the quality of community detection, but also has potential to provide the semantic descriptions of communities, and help to understand the functions of communities [7][8][9][10][11].Existing methods that joint the two types of information can be roughly classified into two categories: model-based methods [12][13][14][15][16][17][18][19][20][21] and other heuristic methods [22][23][24][25][26][27]. Model-based methods are mainly on the basis of probabilistic generative models.…”