Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …

Graph property prediction tasks are important and numerous. While each task offers a small
size of labeled examples, unlabeled graphs have been collected from various sources and …

This paper focuses on graph-level representation learning that aims to represent graphs as
vectors that can be directly utilized in downstream tasks such as graph classification. We …

Leveraging labels in a supervised learning framework as prior knowledge to enhance
network anomaly detection has become a trend. Unfortunately, just a few labels are typically …

Abstract Graph Neural Networks (GNN) is one of the promising machine learning areas in
solving real world problems such as social networks, recommender systems, computer …

This work studies self-supervised graph learning for text-attributed graphs (TAGs) where
nodes are represented by textual attributes. Unlike traditional graph contrastive methods that …

Forests are an invaluable natural resource, playing a crucial role in the regulation of both
local and global climate patterns. Additionally, they offer a plethora of benefits such as …