Graph homophily refers to the phenomenon that connected nodes tend to share similar
characteristics. Understanding this concept and its related metrics is crucial for designing …

In this paper, we propose a novel activation function tailored specifically for graph data in
Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible …

There has been rapid growth in biomedical literature, yet capturing the heterogeneity of the
bibliographic information of these articles remains relatively understudied. Graph neural …

Although existing graph self-supervised learning approaches have paid attention to the
directed nature of networks, they have often overlooked the ubiquitous scale-free attributes …

Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when
we can trust them. We develop a computationally effective approach towards providing …

Attributed network embedding seeks to depict each network node via a compact, low-
dimensional vector while effectively preserving the similarity between node pairs, which lays …

Heterophily, or the tendency of connected nodes in networks to have different class labels or
dissimilar features, has been identified as challenging for many Graph Neural Network …

Graph neural networks (GNNs) have emerged as go-to models for node classification in
graph data due to their powerful abilities in fusing graph structures and attributes. However …

Attributed bipartite graphs (ABGs) are an expressive data model for describing the
interactions between two sets of heterogeneous nodes that are associated with rich …

Abstract Graph Neural Networks (GNNs) have experienced a significant surge in adoption
across various domains, including recommender systems and biology, demonstrating their …