Graph Neural Networks (GNNs) show strong expressive power on graph data
mining, by aggregating information from neighbors and using the integrated
representation in the downstream tasks. The same aggregation methods and
parameters for each node in a graph are used to enable the GNNs to utilize the
homophily relational data. However, not all graphs are homophilic, even in the
same graph, the distributions may vary significantly. Using the same
convolution over all nodes may lead to the ignorance of various graph patterns.
Furthermore, many existing GNNs integrate node features and structure
identically, which ignores the distributions of nodes and further limits the
expressive power of GNNs. To solve these problems, we propose Meta Weight Graph
Neural Network (MWGNN) to adaptively construct graph convolution layers for
different nodes. First, we model the Node Local Distribution (NLD) from node
feature, topological structure and positional identity aspects with the
Meta-Weight. Then, based on the Meta-Weight, we generate the adaptive graph
convolutions to perform a node-specific weighted aggregation and boost the node
representations. Finally, we design extensive experiments on real-world and
synthetic benchmarks to evaluate the effectiveness of MWGNN. These experiments
show the excellent expressive power of MWGNN in dealing with graph data with
various distributions.

本文介绍了一个名为 Meta Weight Graph Neural Network 的模型，该模型可以自适应地构建不同节点的图卷积层来提高图神经网络的表达能力，其通过使用元权重模型来生成自适应图卷积来提高节点表示，并在真实和合成基准测试中进行了广泛的实验证明了其在处理具有不同分布的图数据方面具有出色的表现。