Robust Graph Structure Learning with Virtual Nodes Construction

Wenchuan Zhang, Weihua Ou (), Weian Li, Jianping Gou, Wenjun Xiao and Bin Liu
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Wenchuan Zhang: School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China
Weihua Ou: School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China
Weian Li: School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China
Jianping Gou: College of Computer and Information Science, Southwest University, Chongqing 400700, China
Wenjun Xiao: School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China
Bin Liu: School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China

Mathematics, 2023, vol. 11, issue 6, 1-18

Abstract: Graph neural networks (GNNs) have garnered significant attention for their ability to effectively process graph-related data. Most existing methods assume that the input graph is noise-free; however, this assumption is frequently violated in real-world scenarios, resulting in impaired graph representations. To address this issue, we start from the essence of graph structure learning, considering edge discovery and removal, reweighting of existing edges, and differentiability of the graph structure. We introduce virtual nodes and consider connections with virtual nodes to generate optimized graph structures, and subsequently utilize Gumbel-Softmax to reweight edges and achieve differentiability of the Graph Structure Learning (VN-GSL for abbreviation). We conducted a thorough evaluation of our method on a range of benchmark datasets under both clean and adversarial circumstances. The results of our experiments demonstrate that our approach exhibits superiority in terms of both performance and efficiency. Our implementation will be made available to the public.

Keywords: graph neural networks; graph representation learning; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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