Simplified PCNet with Robustness

arXiv:2403.03676v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs. However, they cannot generalize well to real-world graphs with different levels of homophily. In response, the Possion-Charlier Network (PCNet) \cite{li2024pc}, the previous work, allows graph representation to be learned from heterophily to homophily. Although PCNet alleviates the heterophily issue, there remain some challenges in further improving the efficacy and efficiency. In this paper, we simplify …

abstract arxiv attention cs.lg gnns graph graph neural networks graph representation graphs however network networks neural networks representation robustness simplified success type work world