Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism, which has been demonstrated effective, is the most fundamental part of GNNs. Although ...

Intrusion Detection Systems (IDS) play a crucial role in safeguarding systems and networks from different types of attacks. However, IDSes face significant hurdles in detecting Advanced Persistent Threats (APTs), which are sophisticated cyber-attacks ...

The goal of click-through rate (CTR) prediction in recommender systems is to effectively work with input features. However, existing CTR prediction models face three main issues. First, many models use a basic approach for feature combinations, leading to ...

Treating programs as graphs and employing graph learning techniques to analyze them have been widely adopted in many software engineering tasks. A recent progress in this vein is to apply graph neural networks (GNNs) to model program graphs, which is ...

Many recent methods for the analysis of histology whole slide images (WSIs) have used graph neural networks (GNNs) to aggregate visual information over a large image resolution. However, domain shift is a significant challenge in computational ...

Early diagnosis of lung cancer is critical as it can save people’s lives. Long-range dependencies within volumetric medical images are essential attributes for accurate lung nodule classification. Many deep learning-based methods are used for ...

Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the past few years, due to their novelty and presumably good ...

Traffic trajectories of various vehicles, bicycles and pedestrians can help understand the traffic dynamics in a fine-grained manner like traffic flow, traffic congestion and ride-hailing demand. The comprehensive usage of traffic trajectory data ...

• Semantic-aware graph learning is utilized throughout the entire pipeline.
• Two spatiotemporal gates are devised with semantic graphs.
• The proposed STEGA integrates GNN and powerful generative Transformer.
• The proposed model ...

Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated ...

• Proposing a novel contrastive learning paradigm for multi-behavior recommendation.
• Solving the data sparsity and noise problem simultaneously.
• Adopting SVD method for graph augmentation to enhance model performance.
• Experiments ...

Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization is a commonly used technique, which applies statistics on coverage spectra ...

A vast number of incoming bug reports demand effective methods to identify priority and severity for bug triaging. With increased technological advancement, machine learning and deep learning have been extensively examined to address this problem. ...

Software fault localization in complex systems poses significant challenges. Traditional spectrum-based methods (SBFL) and newer learning-based approaches often fail to fully grasp the software’s complexity. Graph Neural Network (GNN) techniques, which ...

In the current study, a novel network model is proposed for text classification based on Graph Attention Networks (GATs) and sentence-transformer embeddings. Most existing methods with a pretraining model as an input layer still treat words as ...

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• A novel GAT variant with multilevel embeddings as inputs for text classification is proposed.
• Unidirectional message passing strategy to avoid the noise caused by the additional global nodes.
• A SENet-based channel-attention ...

This article presents a novel system, LLDPC,¹ which brings Low-Density Parity-Check (LDPC) codes into Long Range (LoRa) networks to improve Forward Error Correction, a task currently managed by less efficient Hamming codes. Three challenges in achieving ...

Few-shot intent detection is a crucial and challenging problem in task-oriented dialogue systems due to the scarcity of labeled utterances and the emergence of various new intents. Despite recent advances, existing few-shot intent detection methods ...

Optimal mesh refinement is important for finite element simulations, facilitating the generation of non-uniform meshes. While existing neural network-based approaches have successfully generated high quality meshes, they can only handle a fixed ...

The following work presents a new method to solve Poisson’s equation and, more generally, sparse linear systems using graph neural networks. We propose a supervised approach to solve the discretized representation of Poisson’s equation at every ...

Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect ...

In this paper, we introduce a Knowledge-aware Recommender System (KARS) based on Graph Neural Networks that exploit pre-trained content-based embeddings to improve the representation of users and items. Our approach relies on the intuition that textual ...

Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerability characteristics, simplifying the problem into a binary (0-1) ...