Abstract Graph Neural Networks (GNNs) had been demonstrated to be inherently
susceptible to the problems of over-smoothing and over-squashing. These issues prohibit …

While standard graph-theoretic measures have been widely used to characterize atypical
resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired …

Identification of community structures in complex network is of crucial importance for
understanding the system's function, organization, robustness and security. Here, we …

Many complex networks in the real world have community structures–groups of well-
connected nodes with important functional roles. It has been well recognized that the …

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology
positions of labeled nodes, which significantly damages the performance of GNNs. What …

We have performed an empirical comparison of two distinct notions of discrete Ricci
curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci …

Efficient molecular featurization is one of the major issues for machine learning models in
drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC …

This article introduces a new approach to discrete curvature based on the concept of
effective resistances. We propose a curvature on the nodes and links of a graph and present …

Graph neural networks (GNNs) have achieved great success in many graph-based tasks.
Much work is dedicated to empowering GNNs with adaptive locality ability, which enables …

The prevalence of tree-like structures, encompassing hierarchical structures and power law
distributions, exists extensively in real-world applications, including recommendation …