计算机科学
人工智能
深度学习
可微函数
机器学习
水准点(测量)
图形
排列(音乐)
不变(物理)
代表(政治)
理论计算机科学
数学
政治
物理
数学分析
数学物理
声学
法学
政治学
地理
大地测量学
作者
Guohao Li,Chenxin Xiong,Ali Thabet,Bernard Ghanem
标识
DOI:10.1109/tpami.2023.3306930
摘要
Graph Neural Networks (GNNs) have been drawing significant attention to representation learning on graphs. Recent works developed frameworks to train very deep GNNs and showed impressive results in tasks like point cloud learning and protein interaction prediction. In this work, we study the performance of such deep models in large-scale graphs. In particular, we look at the effect of adequately choosing an aggregation function on deep models. We find that GNNs are very sensitive to the choice of aggregation functions (e.g. mean, max, and sum) when applied to different datasets. We systematically study and propose to alleviate this issue by introducing a novel class of aggregation functions named Generalized Aggregation Functions. The proposed functions extend beyond commonly used aggregation functions to a wide range of new permutation-invariant functions. Generalized Aggregation Functions are fully differentiable, where their parameters can be learned in an end-to-end fashion to yield a suitable aggregation function for each task. We show that equipped with the proposed aggregation functions, deep residual GNNs outperform state-of-the-art in several benchmarks from Open Graph Benchmark (OGB) across tasks and domains.
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