Abstract Graph Neural Networks (GNNs) are widely used for analyzing graph-structured
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …

Multimedia content is of predominance in the modern Web era. Investigating how users
interact with multimodal items is a continuing concern within the rapid development of …

The prevalence and perniciousness of fake news has been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …

Multimedia contents are of predominance in the modern Web era. Recent years have
witnessed growing research interests in multimedia recommendation, which aims to predict …

Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …

The prevalence and perniciousness of fake news have been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …

Node representation learning methods, such as graph neural networks, show promising
results when testing and training graph data come from the same distribution. However, the …

Analogy-making gives rise to reasoning, abstraction, flexible categorization and
counterfactual inference--abilities lacking in even the best AI systems today. Much research …

Open Source Software (OSS) is forming the spines of technology infrastructures, attracting
millions of talents to contribute. Notably, it is challenging and critical to consider both the …

Neural networks used for information retrieval tend to capture textual matching signals
between a query and a document. However, neural ranking models for biomedical …