VALSE 论文速览 第177期：基于非线性邻居聚合的鲁棒图神经网络 ...

论文题目：

Generalizing Aggregation Functions in GNNs: Building High Capacity and Robust GNNs via Nonlinear Aggregation

作者列表：

Beibei Wang (安徽大学); Bo Jiang (安徽大学); Jin Tang (安徽大学); Bin Luo (安徽大学)

B站观看网址：

https://www.bilibili.com/video/BV1YJ4m1G7ZE/

论文摘要：

The main aspect powering GNNs is the multi-layer network architecture to learn the nonlinear representation for graph learning task. The core operation in GNNs is the message propagation in which each node updates its information by aggregating the information from its neighbors. Existing GNNs usually adopt either linear neighborhood aggregation (e.g. mean, sum) or max aggregator in their message propagation. (1) For linear aggregators, the whole nonlinearity and network's capacity of GNNs are generally limited because deeper GNNs usually suffer from the over-smoothing issue due to their inherent information propagation mechanism. Also, linear aggregators are usually vulnerable to the spatial perturbations. (2) For max aggregator, it usually fails to be aware of the detailed information of node representations within neighborhood. To overcome these issues, we re-think the message propagation mechanism in GNNs and develop the new general nonlinear aggregators for neighborhood information aggregation in GNNs. One main aspect of our nonlinear aggregators is that they all provide the optimally balanced aggregator between max and mean/sum aggregators. Thus, they can inherit both (i) high nonlinearity that enhances network's capacity, robustness and (ii) detail-sensitivity that is aware of the detailed information of node representations in GNNs' message propagation. Promising experiments show the effectiveness, high capacity and robustness of the proposed methods.

论文链接：

[https://ieeexplore.ieee.org/document/10168286]

代码链接：

[https://github.com/Wangbeibei-AHU/Nonlinear-Aggregation-GNN]

视频讲者简介：

Beibei Wang is currently a Ph.D student in computer science at Anhui University. Her current research interests include graph convolutional neural networks and graph based semi-supervised learning methods.

特别鸣谢本次论文速览主要组织者：

月度轮值AC：张瑞茂 (香港中文大学 (深圳))