Learning Network-to-Network Model for Content-rich Network Embedding

Abstract

Recently, network embedding (NE) has achieved great successes in learning low dimensional representations for network nodes and has been increasingly applied to various network analytic tasks. In this paper, we consider the representation learning problem for content-rich networks whose nodes are associated with rich content information. Content-rich network embedding is challenging in fusing the complex structural dependencies and the rich contents. To tackle the challenges, we propose a generative model, Network-to-Network Network Embedding (Net2Net-NE) model, which can effectively fuse the structure and content information into one continuous embedding vector for each node. Specifically, we regard the content-rich network as a pair of networks with different modalities, i.e., content network and node network. By exploiting the strong correlation between the focal node and the nodes to whom it is connected to, a multilayer recursively composable encoder is proposed to fuse the structure and content information of the entire ego network into the egocentric node embedding. Moreover, a cross-modal decoder is deployed to mapping the egocentric node embeddings into node identities in an interconnected network. By learning the identity of each node according to its content, the mapping from content network to node network is learned in a generative manner. Hence the latent encoding vectors learned by the Net2Net-NE can be used as effective node embeddings. Extensive experimental results on three real-world networks demonstrate the superiority of Net2Net-NE over state-of-the-art methods.