  Graph Neural Networks (GNNs) have been emerging as a promising method for
relational representation including recommender systems. However, various
challenging issues of social graphs hinder the practical usage of GNNs for
social recommendation, such as their complex noisy connections and high
heterogeneity. The oversmoothing of GNNs is an obstacle of GNN-based social
recommendation as well. Here we propose a new graph embedding method
Heterogeneous Graph Propagation (HGP) to tackle these issues. HGP uses a
group-user-item tripartite graph as input to reduce the number of edges and the
complexity of paths in a social graph. To solve the oversmoothing issue, HGP
embeds nodes under a personalized PageRank based propagation scheme, separately
for group-user graph and user-item graph. Node embeddings from each graph are
integrated using an attention mechanism. We evaluate our HGP on a large-scale
real-world dataset consisting of 1,645,279 nodes and 4,711,208 edges. The
experimental results show that HGP outperforms several baselines in terms of
AUC and F1-score metrics.
