MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning

Abstract

Attention mechanism enables the Graph Neural Networks(GNNs) to learn the attention weights between the target node and its one-hop neighbors, the performance is further improved. However, the most existing GNNs are oriented to homogeneous graphs and each layer can only aggregate the information of one-hop neighbors. Stacking multi-layer networks will introduce a lot of noise and easily lead to over smoothing. We propose a Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning method (MHNF). Specifically, we first propose a hybrid metapath autonomous extraction model to efficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level heterogeneous Information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is proposed, which can efficiently integrate different-hop and different-path neighborhood information respectively. This paper can solve the problem of aggregating the multi-hop neighborhood information and can learn hybrid metapaths for target task, reducing the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information of different levels. Experimental results on real datasets show that MHNF is superior to state-of-the-art methods in node classification and clustering tasks (10.94% -69.09% and 11.58% -394.93% relative improvement on average, respectively).

Cross-motif Matching and Hierarchical Contrastive Learning for Recommendation

“…Compared with the ordinary GNN model, as the model deepens, the over-fitting phenomenon will appear earlier. This is also in line with the conclusions of exploration on the depth of model in some previous studies [17,49]. How to solve the over-fitting caused by deep models is also a problem that needs to be further studied in future work about GNNs, especially the Hyper-GNNs.…”

“…Compared with the ordinary GNN model, as the model deepens, the over-fitting phenomenon will appear earlier. This is also the pain point of hypergraph neural network [17,49] that need to be addressed. Therefore, how to solve over-fitting caused by the deep model is also a problem that needs to be further studied in future work about graph neural networks, especially the hypergraph neural networks.…”

“…In recent years, the mining and analysis of graph data has become a research hotspot in the field of data mining and artificial intelligence. Graphs can well represent node attributes and topological structure, which can better model the affinities between nodes [49]. In particular, the emergence of GNNs has accelerated the industrialization process based on graph data research, such as: knowledge question answering and dialogue systems [14,30], recommender systems [2,15,26,31,47], intelligent search [4,20], and etc.…”