graph neural networks

BioSurveillance-Agent: A Multi-Method Explainable Agentic Platform for Epidemic Surveillance on One-Health Networks

How much does the graph help? An information-parity evaluation of graph neural networks for urban air-quality estimation

Graph neural networks (GNNs) are increasingly used to estimate air pollution at unmonitored urban locations, but reported gains often conflate what a model is with what it sees. We present a heterogeneous, wind-aware graph attention network that embeds regulatory monitoring stations and traffic counters as distinct node types, and evaluate it under an information-parity protocol: six geostatistical and tabular baselines receive the identical, published information set, within a task taxonomy separating virtual sensing (target history available) from spatial extrapolation (no target-side data at any stage). On five stations and 16 traffic counters in Graz, Austria (2018-2021), neighboring stations’ same-day concentrations account for essentially the entire previously observed GNN advantage: the architecture gap at parity is -0.002 R2. In leakage-free extrapolation the GNN does not outperform task-legal geostatistics (-0.06 R2 versus the strongest baseline), a heterogeneous GNNExplainer analysis routes almost all explanation mass through station-to-station rather than traffic edges, and split-conformal intervals under-cover (0.53 and 0.76 at nominal 0.90), quantifying the cost of crossstation calibration. In dense daily-resolution urban networks, evaluation practice rather than architecture dominates reported GNN advantages; the protocol, code, and data are released so the audit can be repeated on any network.

Blockchain fog attention framework for collusion detection and automated accountability in internet of things networks

Fog–edge Internet of Things (IoT) systems support low-latency distributed services but remain vulnerable to coordinated attacks that evade detectors designed for independent events. Existing solutions also tend to separate attack detection, provenance verification, and accountability enforcement, leaving no unified path from relational evidence to auditable response. This study aims to develop an integrated framework that detects coordinated malicious behavior, ranks provenance relevance, verifies evidence, and activates rule-based accountability in resource-constrained edge environments. The proposed Blockchain-Fog Computing Collaborative Framework with Deep Attention-based Collusion Detection and Automated Accountability (BF3-ACDA) framework combines a hierarchical blockchain–fog architecture with an attention-based collusion graph neural network (AttnCol-GNN), whose reputation-aware attention coefficient incorporates behavioral correlation and blockchain-derived trust information. A shared attention representation supports both collusion classification and provenance ranking, while Fog-BFT consensus, Merkle verification, and smart contracts provide tamper-evident recording and severity-based enforcement. Across 30 matched independent runs on the collusion-augmented CICIoT2023 benchmark, BF3-ACDA achieved 96.80 ± 0.23% accuracy, 96.75 ± 0.21% F1-score, and 0.975 ± 0.005 AUC-ROC. Direct-transfer accuracy without target-domain fine-tuning was 94.20 ± 0.34% on NSL-KDD and 92.50 ± 0.25% on CICIDS2017. Removing detector-side reputation and verification features reduced accuracy by 3.40 percentage points, whereas replacing learned attention with mean aggregation reduced it by 1.80 points. The INT8 edge model required 12.4 MB and 58.5 ± 3.9 ms per inference. The results demonstrate a method-level coupling of coordinated-pattern detection, provenance relevance, and auditable enforcement, while supporting prototype deployment on evaluated edge, fog, and cloud platforms. As the collusion metadata were constructed for this study, the findings characterize robustness under controlled conditions rather than field performance on naturally occurring collusion.

Multimodal Graph Neural Networks and Evolutionary Knowledge Fusion for Secure and Explainable Governance in Intelligent IoT Interactive Media Systems

This research proposes a secure, explainable, and context-aware governance framework for blockchain-based digital media contracts in multimodal artificial intelligence–enabled AIoT interactive systems. As digital licensing, NFT copyright management, royalty distribution, and cross-chain content circulation become increasingly embedded in smart media ecosystems, existing contract auditing approaches remain limited by unimodal analysis, weak explainability, black-box decision processes, and insufficient cross-platform generalization. To address these challenges, the proposed framework integrates cross-modal data fusion, graph neural networks, and evolutionary expert knowledge fusion to model smart contract code, abstract syntax trees, control-flow graphs, intercontract transactions, cross-chain records, licensing metadata, NFT copyright attributes, and multimodal IoT interaction logs as unified graph representations. By incorporating causal priors and differentiable rules, the framework supports transparent risk reasoning, verifiable explanations, and trustworthy decision support. The study contributes to explainable blockchain security, multimodal AI governance, and intelligent media systems by enabling more robust detection of copyright misuse, unauthorized licensing, abnormal content distribution, and cross-chain transaction risks.

Solar Photovoltaic Generation Forecasting: A Review of Artificial Intelligence Approaches

The rapid global expansion of solar photovoltaic (PV) capacity has increased the operational need for accurate generation forecasting to support grid balancing, dispatch, and market participation. Artificial intelligence (AI) and machine learning (ML) methods now dominate this research area, but the resulting literature is large and methodologically fragmented, making it difficult to establish which methods are used, what data they require, and where the principal gaps lie. This paper combines a bibliometric analysis of 3111 records retrieved from the Web of Science Core Collection (2010–2026) with a technical synthesis of 27 highly cited studies published from 2022 onward, combining the most highly cited works with targeted additions from 2024–2025 covering specific methodological gaps. The bibliometric analysis shows exponential growth in annual output, from three publications in 2010 to 609 in 2025, with keyword evolution tracing a clear methodological trajectory from classical and fuzzy-logic approaches, through shallow and deep neural networks, to transformer- and attention-based architectures since 2023. The technical synthesis finds that classical machine learning remains competitive for day-ahead forecasting with well-structured numerical weather prediction inputs, that convolutional neural network–long short-term memory (CNN-LSTM) hybrids dominate the deep-learning literature, and that graph-based and transformer architectures address multi-site and multi-horizon forecasting, respectively. A comparison of reported results shows that absolute error metrics are not directly comparable across studies due to heterogeneous datasets, metrics, temporal resolutions, and climates, although relative improvements within controlled comparisons are directionally consistent. Seven research gaps are identified, including the absence of standardized benchmarks, limited public dataset availability, weak cross-region generalization, and underdeveloped uncertainty quantification.

Learning Subgroup Relations Using Siamese Graph Neural Networks

Determining whether one finite group is isomorphic to a subgroup of another is a fundamental problem in computational group theory. In this work, we propose a Siamese Graph Neural Network (Siamese GNN) for subgroup prediction using Cayley graph representations of finite groups. Each input group is represented by its undirected Cayley graph and encoded by one branch of a Siamese GNN to produce a graph embedding. The resulting graph embeddings are combined with algebraic features derived directly from the input groups to construct a joint feature vector, which is processed by a fully connected classifier to predict subgroup relations between finite groups. By integrating graph-based structural representations with algebraic features, the proposed framework provides a unified approach for learning subgroup relations from finite groups. Experimental results on an expanded and more diverse dataset of 308 finite-group pairs drawn from 11 group families demonstrate the effectiveness of the proposed architecture, achieving a test BA of 91.67% on an independent test set. Additional experiments evaluate generalization to unseen groups, robustness to different Cayley graph generating sets, the contribution of GNN message passing, performance relative to non-neural baselines, and comparison with exact computational methods. These results illustrate the potential of geometric deep learning for subgroup prediction.

A Parallel Convolutional–Hypergraph Feature learning Network for pulmonary nodule classification

Revisiting topological bias in imbalanced graph learning: A Dual-Balanced Graph Neural Network approach

Learning Calibrated and Transferable Node-Level Infection Risk in Stochastic Epidemics on Complex Networks

基于动态拓扑记忆网络的神经符号推理

This paper proposes a novel approach to neural-symbolic reasoning by introducing a Dynamic Topological Memory Network (DTMN). The core idea is to construct a memory network capable of dynamically adjusting its internal topology to mirror the complexity and relationships within the input data. This addresses a key limitation of existing neural-symbolic methods that often rely on static knowledge graphs or predefined rules, struggling with uncertainty and intricate relationships. The DTMN incorporates a graph-based neural architecture where nodes represent concepts or facts, and edges represent their relationships. A "topological learner" dynamically adjusts the network's structure—including adding, removing, or modifying nodes and edges—using techniques like reinforcement learning or evolutionary algorithms, guided by the input data and existing knowledge. A "symbolic reasoning engine" then leverages this evolving topology for logical inference, generating symbolic expressions as output. The significance lies in the adaptive nature of the network's topology, enabling a more flexible and effective reasoning process compared to traditional methods. We demonstrate the potential of this architecture for robust and efficient symbolic reasoning tasks.

Intelligent Transportation Systems: A Review of Integration of Digital Twin and Machine Learning Control

This paper presents a comprehensive review of the integration of Intelligent Transportation Systems (ITS) and Digital Twin (DT) technologies for intelligent traffic management, and identifies key research gaps and outlines future research directions toward scalable, reliable, adaptive, and real-time DT-enabled ITS architectures for next-generation smart cities.

A Lightweight Explainable AI Framework for Early Brain Tumor Detection Using EEG Functional Connectivity

The findings support the feasibility of lightweight graph-based EEG analysis as an auxiliary screening and decision-support approach and at the same time, MRI and CT remain necessary for clinical confirmation and anatomical characterization.