AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce PPI-Net, a hierarchical graph neural network that integrates protein-protein interaction networks with biological pathway data to predict cancer outcomes and mechanisms. Demonstrating over 90% balanced accuracy across ten cancer types, the model reveals how molecular changes propagate through biological systems to drive disease, offering both predictive power and mechanistic interpretability.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers propose a Hybrid Graph Neural Network (HGNN) for improved EEG-based depression detection that combines fixed and adaptive graph connections to capture both common and individualized brain patterns. The model incorporates a hierarchical pooling mechanism to extract patient-specific brain network information, achieving state-of-the-art results on public datasets.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce HYPER, a foundation model for predicting missing connections in knowledge hypergraphs that can generalize to novel entities and relation types unseen during training. The model advances inductive link prediction by encoding entity positions within hyperedges, enabling transfer learning across relations of varying complexity, with evaluation on 16 new datasets showing consistent outperformance of existing methods.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers present a federated learning approach to detect passive eavesdropping attacks in smart grids by combining graph neural networks with temporal modeling. The system achieves 98.32% per-timestep accuracy while preserving data privacy through decentralized training, addressing a critical vulnerability in grid infrastructure where attackers silently gather topology and consumption data.
AINeutralarXiv – CS AI · May 96/10
🧠Researchers propose using graphlets—small recurring subgraph patterns—as structural tokens for Knowledge Graph Foundation Models (KGFMs), enabling better transfer learning across diverse knowledge graphs. Testing on 51 knowledge graphs demonstrates that this approach outperforms existing KGFMs for zero-shot link prediction tasks.
AINeutralarXiv – CS AI · May 76/10
🧠Researchers introduce NoisyCausal, a benchmark for testing how well large language models handle causal reasoning when presented with noisy, incomplete, or misleading information. The study proposes a modular framework combining LLMs with explicit causal graph structures, demonstrating significant improvements over standard prompting approaches and better generalization across external benchmarks.
AINeutralarXiv – CS AI · May 76/10
🧠Researchers introduce GEM, a novel framework combining Graph Neural Networks, mixture-of-experts routing, and ReAct agents to improve Dialogue State Tracking in multi-domain conversations. The approach achieves 65.19% accuracy on MultiWOZ 2.2, substantially outperforming large language models and existing state-of-the-art methods.
AIBullisharXiv – CS AI · May 76/10
🧠Researchers introduce Graph Tsetlin Machine (GraphTM), an interpretable deep learning approach that processes graph-structured data while maintaining logical explainability. The system demonstrates competitive or superior performance across image classification, action tracking, recommendation systems, and genomic sequence analysis, while training significantly faster than comparable methods like GCNs.
AINeutralarXiv – CS AI · May 16/10
🧠Researchers have formalized Graph World Models (GWMs), a emerging AI paradigm that uses graph structures to represent environments more effectively than traditional tensor-based approaches. The taxonomy categorizes GWMs into three types based on relational inductive biases: spatial (topological), physical (dynamic simulation), and logical (causal reasoning), addressing key limitations like noise sensitivity and error accumulation in classical world models.
AINeutralarXiv – CS AI · Apr 206/10
🧠A comprehensive survey examines how Large Language Models can be effectively integrated with graph-based data structures to improve reasoning, retrieval, and decision-making across domains. The research categorizes integration approaches by purpose, graph type, and strategy, providing practitioners with guidance on selecting appropriate techniques for specific applications in healthcare, finance, robotics, and other fields.
AINeutralarXiv – CS AI · Apr 156/10
🧠Researchers propose GRACE, a dynamic coreset selection framework that reduces LLM training costs by intelligently selecting representative dataset subsets. The method combines representation diversity with gradient-based metrics and uses k-NN graph propagation to adapt to evolving training dynamics, demonstrating improved efficiency across multiple benchmarks.
AINeutralarXiv – CS AI · Apr 156/10
🧠Researchers propose a graph-based soft prompting framework that enables LLMs to reason over incomplete knowledge graphs by processing subgraph structures rather than explicit node paths, achieving state-of-the-art results on multi-hop question-answering benchmarks while reducing computational costs through a two-stage inference approach.
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers propose FedRio, a federated learning framework that enables social media platforms to collaboratively detect bot accounts without sharing raw user data. The system uses graph neural networks, adversarial learning, and reinforcement learning to improve bot detection accuracy while maintaining privacy across heterogeneous platform architectures.
AINeutralarXiv – CS AI · Apr 136/10
🧠Researchers propose GNN-as-Judge, a framework combining Large Language Models with Graph Neural Networks to improve learning on text-attributed graphs in low-resource settings. The approach uses collaborative pseudo-labeling and weakly-supervised fine-tuning to generate reliable labels while reducing noise, demonstrating significant performance gains when labeled data is scarce.
AINeutralarXiv – CS AI · Apr 106/10
🧠Researchers propose G-Defense, a graph-enhanced framework that uses large language models and retrieval-augmented generation to detect fake news while providing explainable, fine-grained reasoning. The system decomposes news claims into sub-claims, retrieves competing evidence, and generates transparent explanations without requiring verified fact-checking databases.
AIBullisharXiv – CS AI · Apr 76/10
🧠Researchers have developed SmartGuard Energy Intelligence System (SGEIS), an AI framework that combines machine learning, deep learning, and graph neural networks to detect electricity theft in smart grids. The system achieved 96% accuracy in identifying high-risk nodes and demonstrates strong performance with practical applications for energy security.
AIBullisharXiv – CS AI · Mar 266/10
🧠Researchers propose MixDemo, a new GraphRAG framework that uses a Mixture-of-Experts mechanism to select high-quality demonstrations for improving large language model performance in domain-specific question answering. The framework includes a query-specific graph encoder to reduce noise in retrieved subgraphs and significantly outperforms existing methods across multiple textual graph benchmarks.
AI × CryptoBullisharXiv – CS AI · Mar 266/10
🤖Researchers developed LineMVGNN, a novel graph neural network method for anti-money laundering that uses multi-view graph learning to analyze transaction networks. The method outperformed existing approaches on real-world datasets including Ethereum phishing networks and financial payment data.
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AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers developed MR-GNF, a lightweight AI model that performs regional weather forecasting using multi-resolution graph neural networks on ellipsoidal meshes. The model achieves competitive accuracy with traditional numerical weather prediction systems while using significantly less computational resources (under 80 GPU-hours on a single RTX 6000 Ada).
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AIBullisharXiv – CS AI · Mar 126/10
🧠Researchers developed a lightweight AI framework for the Game of the Amazons that combines graph attention networks with large language models, achieving 15-56% improvement in decision accuracy while using minimal computational resources. The hybrid approach demonstrates weak-to-strong generalization by leveraging GPT-4o-mini for synthetic training data and graph-based learning for structural reasoning.
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AIBullisharXiv – CS AI · Mar 96/10
🧠Researchers developed an interpretable AI framework for fetal ultrasound image classification that incorporates medical concepts and clinical knowledge. The system uses graph convolutional networks to establish relationships between key medical concepts, providing explanations that align with clinicians' cognitive processes rather than just pixel-level analysis.
AIBullisharXiv – CS AI · Mar 55/10
🧠Researchers have developed HealthMamba, a new AI framework that uses spatiotemporal modeling and uncertainty quantification to predict healthcare facility visits more accurately. The system achieved 6% better prediction accuracy and 3.5% improvement in uncertainty quantification compared to existing methods when tested on real-world datasets from four US states.
AINeutralarXiv – CS AI · Mar 45/102
🧠Researchers propose MANDATE, a Multi-scale Neighborhood Awareness Transformer that improves graph fraud detection by addressing limitations of traditional graph neural networks. The system uses multi-scale positional encoding and different embedding strategies to better identify fraudulent behavior in financial networks and social media platforms.
AINeutralarXiv – CS AI · Mar 36/103
🧠Researchers have developed theoretical foundations for SuperHyperGraph Neural Networks (SHGNNs) and Plithogenic Graph Neural Networks, extending traditional graph neural networks to handle complex hierarchical structures and multi-valued attributes. These advanced frameworks aim to better model uncertainty and higher-order interactions in complex networks beyond the capabilities of standard graph neural networks.
AIBullisharXiv – CS AI · Mar 36/108
🧠Researchers have developed FCN-LLM, a framework that enables Large Language Models to understand brain functional connectivity networks from fMRI scans through multi-task instruction tuning. The system uses a multi-scale encoder to capture brain features and demonstrates strong zero-shot generalization across unseen datasets, outperforming conventional supervised models.
