  Recent years have witnessed the great accuracy performance of graph-based
Collaborative Filtering (CF) models for recommender systems. By taking the
user-item interaction behavior as a graph, these graph-based CF models borrow
the success of Graph Neural Networks (GNN), and iteratively perform
neighborhood aggregation to propagate the collaborative signals. While
conventional CF models are known for facing the challenges of the popularity
bias that favors popular items, one may wonder "Whether the existing
graph-based CF models alleviate or exacerbate popularity bias of recommender
systems?" To answer this question, we first investigate the two-fold
performances w.r.t. accuracy and novelty for existing graph-based CF methods.
The empirical results show that symmetric neighborhood aggregation adopted by
most existing graph-based CF models exacerbate the popularity bias and this
phenomenon becomes more serious as the depth of graph propagation increases.
Further, we theoretically analyze the cause of popularity bias for graph-based
CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to
achieve an accuracy-novelty trade-off by controlling the normalization strength
in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly
applied to the existing graph-based CF backbones without additional
computation. Finally, experimental results on three benchmark datasets show
that our proposed method can improve novelty without sacrificing accuracy under
various graph-based CF backbones.
