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Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering. Inspired by the emerging mutual information-based learning algorithm, in this paper we propose an unsupervised graph neural network Heterogeneous Deep Graph Infomax (HDGI ) for heterogeneous graph representation learning. We use the meta-path to model the structure involving semantics in heterogeneous graphs and utilize graph convolution module and semantic-level attention mechanism to capture individual node local representations. By maximizing the local-global mutual information, HDGI effectively learns highlevel node representations. Experiments show that HDGI remarkably outperforms state-of-the-art unsupervised graph representation learning methods. We even achieve comparable performance in node classification tasks when comparing with state-ofthe-art supervised end-to-end GNN models.

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Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training instances, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter(Active Iterative Alignment), is introduced in this paper. The model ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments were performed on a real-world aligned networks dataset, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.

Graph neural networks (GNNs) have achieved outstanding performance in learning graph-structured data. Many current GNNs suffer from three problems when facing large-size graphs and using a deeper structure: neighbors explosion, node dependence, and oversmoothing. In this paper, we propose a general subgraph-based training framework, namely Ripple Walk Training (RWT), for deep and large graph neural networks. RWT samples subgraphs from the full graph to constitute a mini-batch and the full GNN is updated based on the mini-batch gradient. We analyze the high-quality subgraphs required in a mini-batch in a theoretical way. A novel sampling method Ripple Walk Sampler works for sampling these high-quality subgraphs to constitute the mini-batch, which considers both the randomness and connectivity of the graph-structured data. Extensive experiments on different sizes of graphs demonstrate the effectiveness of RWT in training various GNNs (GCN & GAT).