Neighborhood overlap-aware heterogeneous hypergraph neural network for link prediction

Yifan Lu, Mengzhou Gao, Huan Liu, Zehao Liu, Wei Yu, Xiaoming Li, Pengfei Jiao

Abstract: Highlights•We introduce graph structural information learned from overlapped neighbors to maintain graph topology, thus assisting in link prediction.•We propose NOH that uses a heterogeneous hypergraph variational autoencoder to learn latent node embeddings.•We design a simple but effective structural information generator which can handle overlapped neighborhoods.•NOH consistently achieves state-of-the-art performance compared with the baselines on link prediction.