Distance Enhanced Hypergraph Learning for Dynamic Node Classification

Abstract Dynamic node classification aims to predict the labels of nodes in the dynamic networks. Existing methods primarily utilize the graph neural networks to acquire the node features and original graph structure features. However, these approaches ignore the high-order relationships between nodes and may lead to the over-smoothing issue. To address these issues, we propose a distance enhanced hypergraph learning (DEHL) method for dynamic node classification. Specifically, we first propose a time-adaptive pre-training component to generate the time-aware representations of each node. Then we utilize a dual-channel convolution module to construct the local and global hypergraphs which contain the corresponding local and global high-order relationships. Moreover, we adopt the K-nearest neighbor algorithm to construct the global hypergraph in the embedding space. After that, we adopt the node convolution and hyperedge convolution to aggregate the features of neighbors on the hypergraphs to the target node. Finally, we combine the temporal representations and the distance enhanced representations of the target node to predict its label. In addition, we conduct extensive experiments on two public dynamic graph datasets, i.e., Wikipedia and Reddit. The experimental results show that DEHL outperforms the state-of-the-art baselines in terms of AUC.