Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and network …

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic
based on historical situations. This problem has received ever-increasing attention in …

Graph clustering, which learns the node representations for effective cluster assignments, is
a fundamental yet challenging task in data analysis and has received considerable attention …

Graph-structured data, prevalent in domains ranging from social networks to biochemical
analysis, serve as the foundation for diverse real-world systems. While graph neural networks …

In recent years, deep learning on graphs has achieved remarkable success in various domains.
However, the reliance on annotated graph data remains a significant bottleneck due to …

In this paper, we study semi-supervised graph classification, which aims at accurately
predicting the categories of graphs in scenarios with limited labeled graphs and abundant …

Compared with linear thermoplastic polymers, thermoset polymers with three-dimensional
network structures exhibit improved solvent tolerance, heat resistance, and mechanical …

Based on the characteristics of commodity polymers in large quantities and low costs,
modification of existing commodity polymers emerges as the most effective approach for exploring …

In traditional atom transfer radical polymerization (ATRP), oxygen must be meticulously
eliminated due to its propensity to quench radical species and halt the polymerization process. …

Semi-supervised node classification is a crucial challenge in relational data mining and has
attracted increasing interest in research on graph neural networks (GNNs). However, …