City-scale forecasting is essential for both electric-vehicle (EV) charging operations (e.g., peak management and resource allocation) and urban mobility management (e.g., road-network flow monitoring and incident response). Spatio-temporal graph neu...
Background: Mechanistic reporting from large-scale biomedical knowledge graphs remains challenging, particularly when integrating structured graph evidence with large language model (LLM)–based explanation in a reproducible and auditable manner...
Graph neural networks (GNNs) typically exhibit superior performance in shallow architectures. However, as the network depth increases, issues such as overfitting and oversmoothing of hidden vector representations arise, significantly diminishing mode...
Fraud detection on multi-relational graphs is challenging because real-world fraud-related data often contains heterogeneous relations, noisy structures, and imbalanced labels. Existing GNN-based methods usually rely on predefined graph structures, b...
Graph neural networks (GNNs) are highly effective on relational data, yet their performance degrades sharply when graph topology is poisoned before training. Existing defenses usually assume a fixed attack pattern and a fixed graph structure, which m...
Retrieval-Augmented Generation (RAG) is widely used for long-text summarization due to its efficiency and scalability. However, standard RAG methods flatten documents into independent chunks, disrupting sequential flow and thematic structure, resulti...
Software vulnerabilities are a primary cause of security breaches, and their automated detection at scale has therefore become a pressing concern for both industry and academia. Most Graph Neural Network (GNN) approaches to vulnerability detection tr...
Textual-edge graphs represent interactions through both connectivity and relation-specific text. Learning community structure from these graphs requires an encoder that preserves the relevance of individual relations when combining semantic and struc...
Smart contracts empower many blockchain applications but are exposed to code-level defects. Existing methods do not scale to the evolving code, do not represent complex control and data flows, and lack granular and calibrated evidence. To address the...
Artificial intelligence methods have attracted increasing interest in structural analysis. However, engineering applications still require reliable representation of irregular topology, actual member loads, mechanical consistency, and interpretable s...
Dynamic vehicular integrated sensing and communication (ISAC) requires representations that remain compact, decision-relevant, and structurally stable as mobility rewires interference and sensing relations. This paper presents IC-GMRO, a conditional...
A rapidly expanding multimedia environment in recent years has led to an explosive increase in demand for multimodality that can communicate with humans in various ways. Even though the convergence of vision and language intelligence has shed light o...
Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies...
Predicting ncRNA–protein interactions (NPIs) is essential for understanding regulatory roles in cellular processes and disease mechanisms, yet experimental methods are costly and time-consuming. In this study, we propose NPI-WGNN, a novel weigh...
Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale...
Camera localization aims to estimate the six-degree-of-freedom camera pose from a single RGB image and is essential for robotics, autonomous navigation, and augmented reality. Despite recent progress in learning-based localization, robust pose estima...
Sentiment analysis in Chinese microblogs is challenged by complex syntactic structures and fine-grained sentiment shifts. To address these challenges, a Contextually Enriched Graph Neural Network (CE-GNN) is proposed, integrating self-supervised lear...
Information cascades in online social networks arise from stochastic interactions among user behavior, temporal activation, and graph-structured exposure. Early prediction of cascade outcomes remains difficult because only a short diffusion prefix is...
Predicting mineral deposits accurately requires capturing the complex interactions among geological structures, geochemical anomalies, and alteration patterns. To address this challenge, this study develops a Knowledge–Data Collaboration Graph...
Urban transportation intrusion detection is difficult because many compromised messages remain individually credible until they are checked against surrounding road, sensor, and signal states. This study investigated this problem by formulating it as...
Environmental, Social, and Governance (ESG) text classification is important for applications in sustainable finance. However, it remains a challenging task due to domain terminology and regulatory constraints. While transformer-based models achieve...
We address issues in existing house layout generation methods, including chaotic room layouts, limited iterative refinement, and restricted style diversity. We propose Graph-RWGAN, a generative adversarial network based on a multi-relational graph at...
In real-world applications, the prevalence of heterogeneous graph data has driven the development of heterogeneous graph neural networks (HGNNs) as an effective solution for modeling intricate semantic relationships. A widely adopted strategy involve...
Effectively integrating graph topology and node attributes, while assigning nodes with both semantic similarity and structural closeness to the same community, remains a key challenge in attributed graph community detection. To address this challenge...
Building community discovery aims to identify spatially continuous and cognitively coherent groups of buildings that represent meaningful urban spatial structures. Existing methods mainly integrate geometric similarity, spatial proximity, and topolog...
Traffic flow prediction is a prominent research area in intelligent transportation systems, significantly contributing to urban traffic management and control. Existing methods or models for traffic flow prediction predominantly rely on a fixed-graph...
A knowledge graph is a structured semantic network designed to describe physical entities and relations in the world. A comprehensive and accurate knowledge graph is essential for tasks such as knowledge inference and recommendation systems, making l...
Research on vehicle trajectory prediction based on road monitoring video data often utilizes a global map as an input, disregarding the fact that drivers rely on the road structures observable from their own positions for path planning. This oversigh...
In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. However, this approach often overlooks th...
We propose SAT-GATv2, a graph neural network (GNN)-based model designed to solve the Boolean satisfiability problem (SAT) through graph-based deep learning techniques. SAT-GATv2 transforms SAT formulas into graph structures, leveraging message-passin...
LiDAR point clouds provide accurate and direct representations of spatial locations and geometric structures of objects in 3D space, making them essential for applications such as target recognition in autonomous driving and 3D reconstruction in smar...
Accurate molecular property prediction underpins progress in drug discovery, medicinal chemistry, and molecular design and depends critically on the quality of the learned molecular representations. While Graph Neural Networks (GNNs) and Graph Transf...
The prediction accuracy of RNA’s tertiary structure remains a core challenge in the field of computational biology. Existing models frequently encounter significant challenges due to the complexities of diverse topologies and the intricate natu...
For an automated driving system to be robust, it needs to recognize not only fixed signals such as traffic signs and traffic lights, but also gestures used by traffic police. With the aim to achieve this requirement, this paper proposes a new gesture...
Change point detection in dynamic social graphs aims to identify significant transitions in evolving interaction patterns. The task is particularly challenging due to sparse and noisy interactions and frequent user turnover, while the existing method...
Multi-Hop Knowledge Graph Question Answering (multi-hop KGQA) aims to obtain answers by analyzing the semantics of natural language questions and performing multi-step reasoning across multiple entities and relations in knowledge graphs. Traditional...
Recent GraphRAG methods based on knowledge graphs (KGs) primarily rely on either under-reasoning or a structural path-level retriever, which prevents them from jointly capturing fine-grained semantic relevance and explicit multi-hop reasoning paths....
Reliable prediction of chemical–protein interactions (CPIs) remains a key challenge in drug discovery, especially under sparse or noisy biological data. We present MM-TCoCPIn, a Multi-Modal Topology-aware Chemical–Protein Interaction Netw...
Knowledge graph completion (KGC) necessitates comprehensive modeling of heterogeneous relations by effectively integrating both graph structural information and textual semantics. Current approaches often exhibit fragmented feature utilization or sub...
Hyperspectral sensors provide high-dimensional spectral–spatial observations for land-cover analysis, but reliable classification remains difficult when only a few target-scene labels are available. Cross-domain few-shot hyperspectral image cla...
Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we int...
Community detection in multiplex networks seeks groups of nodes from several types of relations. Attention mechanisms can integrate these relations, but weights based on embedding compatibility do not explicitly measure the community support provided...
In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously t...
Software vulnerabilities pose a severe threat to system security and stability. To reinforce the hierarchical structure of source code, enhance the distinctiveness of embedded features, and reduce feature confusion, we propose CADetect, a vulnerabili...
Solving maximum matching problems in bipartite graphs is critical in fields such as computational biology and social network analysis. This study introduces HybridGNN, a novel Graph Neural Network model designed to efficiently address complex matchin...
Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately foreca...
Understanding the evolutionary mechanisms of dynamic graphs is crucial since dynamic is a basic characteristic of real-world networks. The challenges of modeling dynamic graphs are as follows: (1) Real-world dynamics are frequently characterized by g...
Molecular property prediction is a fundamental task in drug discovery and chemical biology, where effective molecular representations are essential for accurate prediction. Learning transferable motif-level representations remains challenging because...
Detecting phishing webpages is a critical task in the field of cybersecurity, with significant implications for online safety and data protection. Traditional methods have primarily relied on analyzing URL features, which can be limited in capturing...
Ethereum hosts more than 50 million deployed contracts, and cumulative losses from contract exploits exceed three billion US dollars, making automated detection and attacker attribution a practical priority. Existing tool-augmented language models an...
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