  Collaborative Filtering (CF) signals are crucial for a Recommender
System~(RS) model to learn user and item embeddings. High-order information can
alleviate the cold-start issue of CF-based methods, which is modelled through
propagating the information over the user-item bipartite graph. Recent Graph
Neural Networks~(GNNs) propose to stack multiple aggregation layers to
propagate high-order signals. However, the oscillation problem, varying
locality of bipartite graph, and the fix propagation pattern spoil the ability
of multi-layer structure to propagate information. The oscillation problem
results from the bipartite structure, as the information from users only
propagates to items. Besides oscillation problem, varying locality suggests the
density of nodes should be considered in the propagation process. Moreover, the
layer-fixed propagation pattern introduces redundant information between
layers. In order to tackle these problems, we propose a new RS model, named as
\textbf{D}eoscillated \textbf{G}raph \textbf{C}ollaborative
\textbf{F}iltering~(DGCF). We introduce cross-hop propagation layers in it to
break the bipartite propagating structure, thus resolving the oscillation
problem. Additionally, we design innovative locality-adaptive layers which
adaptively propagate information. Stacking multiple cross-hop propagation
layers and locality layers constitutes the DGCF model, which models high-order
CF signals adaptively to the locality of nodes and layers. Extensive
experiments on real-world datasets show the effectiveness of DGCF. Detailed
analyses indicate that DGCF solves oscillation problem, adaptively learns local
factor, and has layer-wise propagation pattern. Our code is available online at
https://github.com/JimLiu96/DeosciRec.
