Attributed Network Embedding based on Mutual Information Estimation

Attributed network embedding (ANE) attempts to represent a network in short code, while retaining information about node topological structures and node attributes. A node's feature and topological structure information could be divided into different local aspects, while in many cases, not all the information but part of the information contained in several local aspects determine the relations among different nodes. Most of the existing works barely concern and identify the aspect influence from network embedding to our knowledge. We attempt to use local embeddings to represent local aspect information and propose InfomaxANE which encodes both global and local embeddings from the perspective of mutual information. The local aspect embeddings are forced to learn and extract different aspect information from nodes' features and topological structures by using orthogonal constraint. A theoretical analysis is also provided to further confirm its correctness and rationality. Besides, to provide complete and refined information for local encoders, we also optimize feature aggregation in SAGE with different structures: feature similarities are concerned and aggregator is seperated from encoder. InfomaxANE is evaluated on both node clustering and node classification tasks (including both transductive and inductive settings) with several benchmark datasets, the results show the outperformance of InfomaxANE over competitive baselines. We also verify the significance of each module in our proposed InfomaxANE in the additional experiment.