Hierarchical Message-Passing Graph Neural Networks

Graph Neural Networks (GNNs) have become a promising approach to machine learning with graphs. Since existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled. One is costly in encoding global information on the graph topology. The other is failing to model meso- and macro-level semantics hidden in the graph, such as the knowledge of institutes and research areas in an academic collaboration network. To deal with these two issues, we propose a novel Hierarchical Message-Passing Graph Neural Networks framework. The main idea is to generate a hierarchical structure that re-organises all nodes in a graph into multi-level clusters, along with intra- and inter-level edge connections. The derived hierarchy not only creates shortcuts connecting far-away nodes so that global information can be efficiently accessed via message passing but also incorporates meso- and macro-level semantics into the learning of node embedding. We present the first model to implement this hierarchical message-passing mechanism, termed Hierarchical Community-aware Graph Neural Network (HC-GNN), based on hierarchical communities detected from the graph. Experiments conducted on eight datasets under transductive, inductive, and few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN models in network analysis tasks, including node classification, link prediction, and community detection

AN APPROACH FOR HESITANT NODE CLASSIFICATION IN OVERLAPPING COMMUNITY DETECTION

Overlapping community detection has recently drawn much attention in the field of social network analysis. In this paper, we propose a notion of hesitant node (HN) in network with overlapping community structure. An HN is a special kind of node that contacts with multiple communities but the communication is not frequent or even accidental, thus its community structure is implicit and its classification is ambiguous. Besides, HNs are not rare to be found in networks and may even take up a large number of the nodes in the network, just like the long tail. They should either be classified into certain communities which would promote their development in the network or regarded as the hubs if they are the efficient junctions between different communities. Current approaches have difficulties in identifying and processing HNs. In this paper, a quantitative method based on the Density-Based Rough Set Model (DBRSM) is proposed by combining the advantages of density-based algorithms and rough set model. Our experiments on the real-world and synthetic datasets show the advancement of our approach. HNs are classified into communities which are more similar with them and the classification process enhances the modularity as well