Abstract 3D point clouds are a crucial type of data collected by LiDAR sensors and widely
used in transportation applications due to its concise descriptions and accurate localization …

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

This paper focuses on graph-level representation learning that aims to represent graphs as
vectors that can be directly utilized in downstream tasks such as graph classification. We …

Recently, deep clustering utilizing Graph Neural Networks has shown good performance in
the graph clustering. However, the structure information of graph was underused in existing …

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

Recently, Graph Neural Networks (GNNs) have become a mainstream recommender system
method, where it captures high-order collaborative signals between nodes by performing …

Social recommendation is gaining increasing attention in various online applications,
including e-commerce and online streaming, where social information is leveraged to …

Graph neural network-based collaborative filtering methods have achieved excellent
performance in recommender systems. However, previous works have primarily focused on …

The recommendation algorithm is an important means to alleviate the information explosion
in the era of big data. There has been a great deal of research into the use of knowledge …

Complex networks necessitate the identification of key nodes owing to their ubiquity across
the network. Traditional methodologies, such as machine learning-based and centrality …