Abstract
The rapid development of techniques results in a growing diversity of social network data which require for analysis. Therefore, the deeper understanding of latent knowledge representing the social network data needs learning by combining the insights obtained from multiple, diverse networks carrying heterogeneous information featuring the interrelationship between vertices. In this manuscript, we propose a novel deepmodel- based approach to learn latent structural representation from multi-domain social network data. The algorithm, which we call Deep Multiple Networks Fusion (DMNF), is able to discover an aggregated deep representation, by taking into consideration multiple networks, which represent heterogeneous information carried by the social network data. To perform the task, DMNF first constructs a network representing the total degree of interrelationship between pairwise vertices by utilizing a fusion method to compute such degree taking into consideration heterogeneous information embedded in the network data, e.g., node connection, and attribute relativity. Given the fused network data, DMNF attempts to learn the latent network representation making use of a deep neural network model. Such learned representation is able to reveal the latent structure, e.g., social communities, and clusters in the social network. DMNF has been tested with two sets of real social network data and compared with several prevalent approaches to network community detection. The experimental results show that the latent representation found by DMNF may match well with the ground-truth communities and DMNF is able to outperform the state-of-the-art approaches to detecting social network communities.