  Graph Neural Networks (GNNs) have recently been utilized to build
Collaborative Filtering (CF) models to predict user preferences based on
historical user-item interactions. However, there is relatively little
understanding of how GNN-based CF models relate to some traditional Network
Representation Learning (NRL) approaches. In this paper, we show the
equivalence between some state-of-the-art GNN-based CF models and a traditional
1-layer NRL model based on context encoding. Based on a Markov process that
trades off two types of distances, we present Markov Graph Diffusion
Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based
CF models. Instead of considering the GNN as a trainable black box that
propagates learnable user/item vertex embeddings, we treat GNNs as an
untrainable Markov process that can construct constant context features of
vertices for a traditional NRL model that encodes context features with a
fully-connected layer. Such simplification can help us to better understand how
GNNs benefit CF models. Especially, it helps us realize that ranking losses
play crucial roles in GNN-based CF tasks. With our proposed simple yet powerful
ranking loss InfoBPR, the NRL model can still perform well without the context
features constructed by GNNs. We conduct experiments to perform detailed
analysis on MGDCF.
