Robustness of Graph Neural Networks

Dear Colleagues,

The use of deep learning on graph-structured data has seen a significant rise in popularity in recent years. Among the various techniques for graph representation learning, Graph Neural Networks (GNNs) stand out as the most promising. They have been widely adopted in numerous applications, including, but not limited to, social network analysis, recommender systems, financial fraud detection, combinatorial optimization, knowledge graph embedding, drug discovery, and materials discovery.

The primary objective of this Special Issue is to assemble experts from diverse disciplines to showcase their innovative methodologies and models that enhance the robustness of GNNs. While GNNs have achieved initial successes across a broad spectrum of applications, they can often be overly sensitive to noise and biases present in real-world datasets. This sensitivity can result in suboptimal performance and potentially lead to adverse societal implications. As such, the development of more robust GNNs is crucial for their effective implementation in practical scenarios. In the context of this Special Issue, we will discuss various aspects, including the development of novel robust GNN architectures; the robustness analysis of GNNs in relation to noise and dataset biases; and the examination of GNN robustness from the perspectives of entropy and information theory. We will also delve into the scalability and reliability of GNNs on large-scale graph datasets, their adversarial robustness to attacks, out-of-distribution samples and group transformations (equivariance), as well as the application of robust GNN algorithms in social network analysis, point cloud, biology and chemistry-related fields.

Prof. Dr. Irwin King
Guest Editor

Dr. Ziqiao Meng
Guest Editor Assistant

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