Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using
graph structures based on the relational inductive bias (homophily assumption).
While GNNs have been commonly believed to outperform NNs in real-world tasks,
recent work has identified a non-trivial set of datasets where their
performance compared to NNs is not satisfactory. Heterophily has been
considered the main cause of this empirical observation and numerous works have
been put forward to address it. In this paper, we first revisit the widely used
homophily metrics and point out that their consideration of only graph-label
consistency is a shortcoming. Then, we study heterophily from the perspective
of post-aggregation node similarity and define new homophily metrics, which are
potentially advantageous compared to existing ones. Based on this
investigation, we prove that some harmful cases of heterophily can be
effectively addressed by local diversification operation. Then, we propose the
Adaptive Channel Mixing (ACM), a framework to adaptively exploit aggregation,
diversification and identity channels node-wisely to extract richer localized
information for diverse node heterophily situations. ACM is more powerful than
the commonly used uni-channel framework for node classification tasks on
heterophilic graphs and is easy to be implemented in baseline GNN layers. When
evaluated on 10 benchmark node classification tasks, ACM-augmented baselines
consistently achieve significant performance gain, exceeding state-of-the-art
GNNs on most tasks without incurring significant computational burden.

本文通过重新评估广泛使用的同质性度量，并从后集成节点相似性的角度研究了异质性。提出了一种新的度量方法，并证明了 local diversification operation 可以有效解决一些有害的异质性情况，然后提出了 Adaptive Channel Mixing（ACM）框架来自适应地利用聚合，多样性和标识通道，从而从多样化的节点异质性情况中提取更丰富的局部信息。经过 10 项基准节点分类任务的评估，ACM-augmented 基线始终实现了显着的性能提升，超过大多数任务上的最新 GNN，而不会带来显着的计算负担。

重新审视异质性对图神经网络的影响

Revisiting Heterophily For Graph Neural Networks

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the
graph structures based on the relational inductive bias (homophily assumption).
Though GNNs are believed to outperform NNs in real-world tasks, performance
advantages of GNNs over graph-agnostic NNs seem not generally satisfactory.
Heterophily has been considered as a main cause and numerous works have been
put forward to address it. In this paper, we first show that not all cases of
heterophily are harmful for GNNs with aggregation operation. Then, we propose
new metrics based on a similarity matrix which considers the influence of both
graph structure and input features on GNNs. The metrics demonstrate advantages
over the commonly used homophily metrics by tests on synthetic graphs. From the
metrics and the observations, we find some cases of harmful heterophily can be
addressed by diversification operation. With this fact and knowledge of
filterbanks, we propose the Adaptive Channel Mixing (ACM) framework to
adaptively exploit aggregation, diversification and identity channels in each
GNN layer to address harmful heterophily. We validate the ACM-augmented
baselines with 10 real-world node classification tasks. They consistently
achieve significant performance gain and exceed the state-of-the-art GNNs on
most of the tasks without incurring significant computational burden.

本文提出了 Adaptive Channel Mixing 框架，利用聚合，多样化和恒等通道来以自适应的方式解决 Graph Neural Networks 中的有害异质性问题，并在 10 个真实世界节点分类任务中获得了显着的性能提升。