Timezone: »

 
Poster
$p$-Laplacian Based Graph Neural Networks
Guoji Fu · Peilin Zhao · Yatao Bian

Wed Jul 20 03:30 PM -- 05:30 PM (PDT) @ Hall E #438
in Poster Session 2 »
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and topological information simultaneously. However, most GNNs implicitly assume that the labels of nodes and their neighbors in a graph are the same or consistent, which does not hold in heterophilic graphs, where the labels of linked nodes are likely to differ. Moreover, when the topology is non-informative for label prediction, ordinary GNNs may work significantly worse than simply applying multi-layer perceptrons (MLPs) on each node. To tackle the above problem, we propose a new $p$-Laplacian based GNN model, termed as $^p$GNN, whose message passing mechanism is derived from a discrete regularization framework and could be theoretically explained as an approximation of a polynomial graph filter defined on the spectral domain of $p$-Laplacians. The spectral analysis shows that the new message passing mechanism works as low-high-pass filters, thus making $^p$GNNs are effective on both homophilic and heterophilic graphs. Empirical studies on real-world and synthetic datasets validate our findings and demonstrate that $^p$GNNs significantly outperform several state-of-the-art GNN architectures on heterophilic benchmarks while achieving competitive performance on homophilic benchmarks. Moreover, $^p$GNNs can adaptively learn aggregation weights and are robust to noisy edges.
Keywords: DL: Graph Neural Networks

Author Information

Guoji Fu (Tencent AI Lab)
Peilin Zhao (Tencent AI Lab)
Yatao Bian (Tencent AI Lab)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors