While many existing graph neural networks (GNNs) have been proven to perform $\ell_2 $-
based graph smoothing that enforces smoothness globally, in this work we aim to further …

Graph neural networks (GNNs) have shown the power in graph representation learning for
numerous tasks. In this work, we discover an interesting phenomenon that although residual …

Despite the recent success of graph neural networks (GNN), common architectures often
exhibit significant limitations, including sensitivity to oversmoothing, long-range …

Recent works have demonstrated the benefits of capturing long-distance dependency in
graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long …

Hypergraphs are a powerful abstraction for representing higher-order interactions between
entities of interest. To exploit these relationships in making downstream predictions, a …

Among the many variants of graph neural network (GNN) architectures capable of modeling
data with cross-instance relations, an important subclass involves layers designed such that …

It has been observed that graph neural networks (GNN) sometimes struggle to maintain a
healthy balance between the efficient modeling long-range dependencies across nodes …

For supervised learning with tabular data, decision tree ensembles produced via boosting
techniques generally dominate real-world applications involving iid training/test sets …

Graph neural networks (GNNs) for link prediction can loosely be divided into two broad
categories. First, node-wise architectures pre-compute individual embeddings for each node …

Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy
function serves as input features to an upper-level objective of interest. These optimal …