Learning item representation is crucial for a myriad of on-line e-commerce applications. The nucleus of retail item representation learning is how to properly fuse the semantics within a single item, and the interactions across different items generated ...

Information diffusion prediction aims to forecast the path of information spreading in social networks by exploiting user correlations or preferences. Recent works focus on characterizing the dynamic of user preferences and propose to capture users' ...

Two-view correspondence pruning aims to accurately remove incorrect correspondences (outliers) from initial ones. Graph Neural Networks (GNNs) incorporated by Multilayer Perceptrons (MLPs) are treated as a powerful manner to handle sparse and unevenly ...

As one of the most fundamental computer vision problems, image feature matching aims to establish correct correspondences between two-view images. Existing studies enhance the descriptions of feature points with graph neural network (GNN), identifying ...

Cartoon animal parsing aims to segment the body parts such as heads, arms, legs and tails of cartoon animals. Different from previous parsing tasks, cartoon animal parsing faces new challenges, including irregular body structures, abstract drawing styles ...

This paper proposes a mobile application vulnerability detection method based on Code Property Graphs (CPG) and adaptive graph neural networks. The method first converts source code into CPGs, then uses CodeBERT to vectorize CPG nodes. Subsequently, high-...

Binary code similarity detection(BCSD), as a fundamental technique in software security, has various applications, including malware family detection, known vulnerability detection and code plagiarism detection. Recent deep learning-based BCSD approaches ...

In collaborative software development, developers generally make code changes and commit the changes to the repositories. Among others, "making small, single-purpose commits" is considered the best practice for making commits, allowing the team to ...

Peer code review has become a must-have in modern software development. However, many code review requests (CRRs) could be a backlog for large-scale and active projects, blocking continuous integration and continuous delivery (CI/CD). Prioritizing CRRs ...

This paper presents a novel approach for predicting Power Conversion Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD: synergizing molecular Graphs and Language Descriptors for enhanced PCE prediction. Due to the lack of high-quality ...

Graphs serve as fundamental representations for a diverse array of complex systems, capturing intricate relationships and interactions between entities. In many real-world scenarios, graphs exhibit non-homophilous, or heterophilous, characteristics, ...

Graph-based fraud detection faces significant challenges, such as severe class imbalance, inconsistent connections due to the scarcity of fraudulent nodes, and the camouflage of these nodes appearing like benign nodes. Existing studies often adopt the ...

Factor models, originating in finance for asset pricing, are fundamental tools in quantitative investment. Recently, there has been a trend towards adopting more flexible machine learning approaches instead of previous linear models. However, traditional ...

Knowledge Graph (KG) has proven its effectiveness in recommendation systems. Recent knowledge-aware recommendation methods, which utilize graph neural networks and contrastive learning, underestimate two issues: 1) The neglect of modeling the latent ...

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challenges for fake news detection on social media is how to detect fake news on ...

Anomalies in graphs involve attributes and structures and may occur at different levels (e.g., node or community). Existing GNN-based detection methods often merely focus on anomalies of single nodes or neighborhoods, making it hard to cope with complex ...

Enabling various parties to share data enhances online fraud detection capabilities considering fraudsters tend to reuse resources attacking multiple platforms. Multi-party computation (MPC) techniques, such as secret sharing, offer potential privacy-...

Current adversarial defense methods for GNNs exhibit critical limitations obstructing real-world application: 1) inadequate adaptability to graph heterophily, 2) absent generalizability to early GNNs like GraphSAGE used downstream, and 3) low inference ...

As real-world graph data continues to grow larger and larger, training large graphs in a distributed environment is becoming increasingly prevalent. However, network transmission in a distributed environment can hinder subsequent training steps, ...

In graph anomaly detection (GAD), the fact that anomalous nodes usually exhibit high heterophily, while most Graph Neural Networks (GNNs) have homophily assumptions, leads to poor performance. Many studies have attempted to solve this problem by ...