Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

In recent years, remarkable achievements have been made in artificial intelligence tasks
and applications based on deep neural networks (DNNs), especially in the fields of vision …

Fine-grained traffic forecasting is crucial for the management of urban transportation
systems. Road segments and intersection turns, as vital elements of road networks, exhibit …

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 …

Click-through rate (CTR) prediction plays a critical role in recommender systems and other
applications. Recently, modeling user behavior sequences attracts much attention and …

In e-commerce, fake reviewers frequently post fake reviews to mislead consumers into
making unwise shopping decisions, seriously affecting customers' benefits. Graph neural …

Graph convolutional network (GCN) has gained widespread attention in semisupervised
classification tasks. Recent studies show that GCN-based methods have achieved decent …

The growing number of publications in the field of artificial intelligence highlights the need
for researchers to enhance their efficiency in searching for relevant articles. Most paper …

Graph classification is a critical task in numerous multimedia applications, where graphs are
employed to represent diverse types of multimedia data, including images, videos, and …

Science progresses by building upon the prior body of knowledge documented in scientific
publications. The acceleration of research makes it hard to stay up-to-date with the recent …