Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Abstract Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining
deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its …

This work provides the first theoretical study on the ability of graph Message Passing Neural
Networks (gMPNNs)---such as Graph Neural Networks (GNNs)---to perform inductive out-of …

In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …

Real-world planning problems often involve hundreds or even thousands of objects,
straining the limits of modern planners. In this work, we address this challenge by learning to …

Large language models (LLMs) have revolutionized many areas (eg natural language
processing, software engineering, etc.) by achieving state-of-the-art performance on …

Subgraph-based graph representation learning (SGRL) has been recently proposed to deal
with some fundamental challenges encountered by canonical graph neural networks …

Link prediction is an important and frequently studied task that contributes to an
understanding of the structure of knowledge graphs (KGs) in statistical relational learning …