Information dissemination refers to how information spreads among users on social
networks. With the widespread application of mobile communication and internet …

Temporal Graph Networks (TGNs) have shown remarkable performance in learning
representation for continuous-time dynamic graphs. However, real-world dynamic graphs …

Abstract In recent years, Multimodal Aspect-Based Sentiment Analysis (MABSA) has
garnered attention from researchers. The MABSA technology can effectively perform Aspect …

This paper introduces a subgraph sampling method based on curvature to train large-scale
graphs via mini-batch training. Owing to the difficulty in sampling globally optimal subgraphs …

Abstract Graph Neural Network (GNN) models are recently proposed to process the graph-
structured data for the learning tasks on graphs, eg, node classification, link prediction, and …

Group link prediction is of both theoretical and practical significance since it can be used to
analyze relationships between individuals and groups. However, obeying the homophily …

In this paper, we propose an unbiased graph neural network learning method called
FairScene for indoor scene synthesis. Conventional methods directly apply graphical …

PyKale is a Python library for Knowledge-aware machine learning from multiple sources of
data to enable/accelerate interdisciplinary research. It embodies green machine learning …

With advanced imaging, sequencing, and profiling technologies, multiple omics data
become increasingly available and hold promises for many healthcare applications such as …

Abstract Heterogeneous Information Networks (HINs) are crucial in various intelligent
systems. The latest advancements in HIN learning aim to combine meta-paths and …