TY - JOUR AU - Zhu, Shichao AU - Zhou, Lewei AU - Pan, Shirui AU - Zhou, Chuan AU - Yan, Guiying AU - Wang, Bin PY - 2020/04/03 Y2 - 2024/03/28 TI - GSSNN: Graph Smoothing Splines Neural Networks JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 04 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v34i04.6185 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6185 SP - 7007-7014 AB - <p>Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer from two limitations for graph representation learning. First, they exploit non-smoothing node features which may result in suboptimal embedding and degenerated performance for graph classification. Second, they only exploit neighbor information but ignore global topological knowledge. Aiming to overcome these limitations simultaneously, in this paper, we propose a novel, flexible, and end-to-end framework, Graph Smoothing Splines Neural Networks (GSSNN), for graph classification. By exploiting the smoothing splines, which are widely used to learn smoothing fitting function in regression, we develop an effective feature smoothing and enhancement module Scaled Smoothing Splines (S<sup>3</sup>) to learn graph embedding. To integrate global topological information, we design a novel scoring module, which exploits closeness, degree, as well as self-attention values, to select important node features as knots for smoothing splines. These knots can be potentially used for interpreting classification results. In extensive experiments on biological and social datasets, we demonstrate that our model achieves state-of-the-arts and GSSNN is superior in learning more robust graph representations. Furthermore, we show that S<sup>3</sup> module is easily plugged into existing GNNs to improve their performance.</p> ER -