Graph neural networks (GNNs) have shown state-of-the-art performances in various
applications. However, GNNs often struggle to capture long-range dependencies in graphs …

The prevalence of graph-based data has spurred the rapid development of graph neural
networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets …

Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

Abstract Graph Neural Networks (GNNs) have become the de-facto standard tool for
modeling relational data. However, while many real-world graphs are directed, the majority …

Recovering global rankings from pairwise comparisons has wide applications from time
synchronization to sports team ranking. Pairwise comparisons corresponding to matches in …

Signed and directed networks are ubiquitous in real-world applications. However, there has
been relatively little work proposing spectral graph neural networks (GNNs) for such …

Networks are ubiquitous in many real-world applications (eg, social networks encoding
trust/distrust relationships, correlation networks arising from time series data). While many …

The goal of directed network embedding is to represent the nodes in a given directed
network as embeddings that preserve the asymmetric relationships between nodes. While a …

This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN)
capable of handling both undirected and directed graphs with weights not restricted in sign …

Graph structural information such as topologies or connectivities provides valuable
guidance for graph convolutional networks (GCNs) to learn nodes' representations. Existing …