Feature-Enhanced Latent Summarization Model of Heterogeneous Network

Abstract

With the rapid growth of network data, large-scale heterogeneous network data storage and network repre-sentation have become hot research topics. This paper proposes two different tasks, generating graph summarization and generating node representations of graphs. The target of the graph summarization is to find a compact repre-sentation of the input graph for compressed storage and accelerated query. And the structural information in network data can be extracted well via the network representation, and embedding representation for downstream tasks can be generated. However, in large-scale network data, there are still some challenges to be solved in generating the summarization and embedding representations of graphs. To overcome the problems of the scientific computing and storage space caused by large-scale heterogeneous network, this paper proposes a new feature-enhanced latent sum-marization representation model (FELS), which can obtain the embedding of large-scale network data by the incor-poration of node features and attributes of graphs. Firstly, this paper utilizes different node features of the original graph as basic features and applies a variety of relational operators to capture high-order sub-graph structure infor-mation. Secondly, according to different graph attributes, the potential subspace of the context structural information is learned through a special mapping method. Finally, this paper gets the latent summary representation of the hetero-geneous network through applying matrix decomposition to the learned features of the context, and the latent graph summary representation is a kind of compact latent graph summarization which is independent of the size and dimen-sionality of the input graph, and also able to obtain the node representation. Experimental results show that FELS can gain better potential summarization compared with traditional methods while it has lower model complexity, and FELS achieves higher efficiency and accuracy in link prediction.

Keywords

• |latent summarization|network representation|structure learning|relational operators|feature selection