Timezone: »

 
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs
Yijun Tian · Chuxu Zhang · Zhichun Guo · Xiangliang Zhang · Nitesh Chawla

@
in New Frontiers in Graph Learning »
Event URL: https://openreview.net/forum?id=nT897hw-hHD »

While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data, they are difficult to be deployed in real applications due to the scalability constraint imposed by multi-hop data dependency. Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on node content features using labels derived from trained GNNs. Even though the performance of MLPs can be significantly improved, two issues prevent MLPs from outperforming GNNs and being used in practice: the ignorance of graph structural information and the sensitivity to node feature noises. In this paper, we propose to learn NOise-robust Structure-aware MLPs On Graphs (NOSMOG) to overcome the challenges. Specifically, we first complement node content with position features to help MLPs capture graph structural information. We then design a novel representational similarity distillation strategy to inject structural node similarities into MLPs. Finally, we introduce the adversarial feature augmentation to ensure stable learning against feature noises and further improve performance. Extensive experiments demonstrate that NOSMOG outperforms GNNs and the state-of-the-art method in both transductive and inductive settings across 7 datasets, while maintaining a competitive inference efficiency.

Keywords: knowledge distillation · graph neural networks · MLPs

Author Information

Yijun Tian (University of Notre Dame)
Chuxu Zhang (Brandeis University)
Zhichun Guo (University of Notre Dame)
Xiangliang Zhang (University of Notre Dame)

I am an Associate Professor in the department of Computer Science and Engineering at University of Notre Dame, where I am leading a Machine Intelligence and kNowledge Engineering (MINE) group. My research broadly addresses ways that enable ​computer machines to​ learn by the use of diverse types of data. Specifically, I am interested in designing machine learning algorithms for learning from complex and large-scale streaming data and graph data, with applications to recommendation systems, knowledge discovery, and natural language understanding. More information can be found in the publications grouped by research problems, or the full list of over 190 peer-reviewed papers. I was invited to deliver an Early Career Spotlight talk at IJCAI-ECAI 2018. In 2010, I received a Chinese government award for outstanding self-financed students abroad. In 2009, I was awarded the European Research Consortium for Informatics and Mathematics (ERCIM) Alain Bensoussan Fellowship. I regularly serve on the Program Committee for premier conferences like SIGKDD (Senior PC), AAAI (Area Chair, Senior PC), IJCAI (Area Chair, Senior PC), etc. I also serve as Editor-in-Chief of ACM SIGKDD Explorations, associated editor for IEEE Transactions on Dependable and Secure Computing (TDSC) and Information Sciences. Prior to joining the University of Notre Dame, I was an Associate Professor in Computer Science at KAUST, Saudi Arabia. I completed my Ph.D. degree in computer science from INRIA-University Paris-Sud, France, in July 2010. I received my master and bachelor degrees from Xi’an Jiaotong University, China.

Nitesh Chawla (University of Notre Dame)

More from the Same Authors