Abstract Graph Neural Networks (GNNs) have recently been applied to graph learning tasks
and achieved state-of-the-art (SOTA) results. However, many competitive methods run …

Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …

Many network applications can be formulated as NP-hard combinatorial optimization
problems of community detection (CD) that partitions nodes of a graph into several groups …

Graph neural network (GNN) is a powerful model for learning from graph data. However,
existing GNNs may have limited expressive power, especially in terms of capturing …

While most network embedding techniques model the proximity between nodes in a
network, recently there has been significant interest in structural embeddings that are based …

Existing causal models for link prediction assume an underlying set of inherent node factors—
an innate characteristic defined at the node's birth—that governs the causal evolution of …

In this paper, we explore the use of Graph Neural Networks (GNNs) for anomaly anticipation
in high performance computing (HPC) systems. We propose a GNN-based approach that …

Self-supervised learning (SSL) for graphs is an essential problem since graph data are
ubiquitous and labeling can be costly. We argue that existing SSL approaches for graphs …

Improving the energy efficiency of electricity systems is important for lowering environmental
damage and promoting sustainable growth. In recent years, reinforcement learning (RL) …

The network embedding task is to represent a node in a network as a low-dimensional
vector while incorporating the topological and structural information. Most existing …