#graph neural networks
82 resultsNo Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk
Tsuyoshi Iwata, Johannes Laurmaa, Ryohei Hisano
The paper proposes a visibility‑aware graph neural network that leverages corporate ownership relationships to predict business conduct risk for firms lacking recorded incidents, t…
AtmosERC: Modeling Dialogue-Level Affective Atmosphere for Emotion Recognition in Conversation
Weijie Feng, Tongwei Zhang, Binbin Liu +1
The paper introduces AtmosERC, a graph-based framework that captures a dialogue-level affective atmosphere to improve emotion recognition in conversations, providing both lightweig…
Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions
Truong Giang Vu, Li Yang, Richard W. Pazzi
The paper surveys how neural architecture search techniques are used to automatically design deep learning models for traffic prediction, reviewing gradient‑based, evolutionary, an…
Multi-User Localization via Active Sensing with Electromagnetically Reconfigurable Antennas
Ruizhi Zhang, Yuchen Zhang, Ying Zhang +1
The paper proposes an active‑sensing framework for multi‑user uplink localization that adaptively configures shared electromagnetically reconfigurable antennas using past pilot obs…
Schreier-Coset Graph Rewiring
Aryan Mishra, Randy Martinez, Lizhen Lin
The paper proposes Schreier-Coset Graph Rewiring, a group‑theoretic method that augments a graph with a Schreier‑Coset graph to reduce over‑squashing in graph neural networks while…
THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model
Yixin Peng, Diego Collarana, Er Jin +1
The paper introduces THGFM, a dual-branch graph transformer model that jointly handles structural heterogeneity and temporal dynamics in heterogeneous graphs using shared and speci…
Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems
Xu Zheng, Zhuomin Chen, Chaohao Lin +4
The paper introduces Trajectory Graph Copilot, a framework that builds probabilistic graphs of past agent trajectories and uses a graph neural network to flag potentially erroneous…
Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks
Xin Xu, Siru Tao, Kaizhen Tan
The paper shows that gradient‑based explainers for graph neural networks can produce arbitrary top‑k edge explanations when the input graph has nontrivial automorphisms, and provid…
Learning the Word Problem: Geodesic Lengths and Cryptographic Applications
Elisabeth Fink
The paper presents WPNet, a graph neural network that learns to solve the Word Problem and predict geodesic lengths in certain infinite groups, and shows how this can be used to at…
TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation
He Ma, Chen Liu
The paper introduces TRWH, a framework that combines large language model‑generated text profiles with heterogeneous graph neural networks and random‑walk augmentation to improve r…
MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation
Jiahao Tian, Zhenkai Wang
The paper presents MARS, a modular multi‑agent framework that combines large language model reasoning with lightweight collaborative retrieval and geospatial filtering to re‑rank r…
Accelerated descriptor-free path sampling for protein-ligand binding kinetics
Simon M. Lichtinger, Roberto Covino
The paper introduces a descriptor‑free path‑sampling method that uses an equivariant graph neural network to model the committor and a static bias potential to accelerate convergen…
Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs
Laura Baird, Armin Moin
The paper introduces a graph‑learning method that uses Software Bill of Materials (SBOM) graphs to predict cascades of multiple vulnerabilities in software supply chains, combining…
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
Zlata Kikteva, Artur Romazanov, Annette Hautli-Janisz +1
The paper introduces a method that extracts reasoning structures from LLM‑generated text and uses a graph neural network to attribute authorship, showing higher robustness to parap…
Octopus-inspired Distributed Control for Soft Robotic Arms: A Graph Neural Network-Based Attention Policy with Environmental Interaction
Linxin Hou, Qirui Wu, Zhihang Qin +2
The paper introduces SoftGM, a graph‑neural‑network based distributed control system for segmented soft robotic arms that learns to reach targets while discovering obstacles online…
DAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification
Pengkun Wang, Weijia Cao, Ning Wang +1
The paper proposes DAPGNet, a graph diffusion network that incorporates physical priors from contiguous spectral bands to improve hyperspectral image classification, using adaptive…
A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
Raffaele Marino, Lorenzo Buffoni, Bogdan Zavalnij
The paper surveys algorithms for solving the maximum clique problem, covering classical exact and heuristic methods as well as recent graph neural network and quantum computing app…
Training a force field for proteins and small molecules from scratch
Alexandre Blanco-González, Thea K Schulze, Evianne Rovers +1
The paper introduces Garnet, a graph neural network that learns force field parameters for proteins and small molecules directly from quantum mechanical and experimental data, achi…
SevenNet-Polar for MultiTask Prediction of Energy, Forces, Stress, and Born Effective Charges: Development and Application to ZrO$_2$, Li$_3$PO$_4$, and Perovskites
Anh Khoa Augustin Lu, Shungo Arai, Yutack Park +3
The paper introduces SevenNet-Polar, an equivariant graph neural network that simultaneously predicts energy, forces, stress, and Born effective charge tensors with high accuracy,…
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
Yuchang Zhu, Zezhong Xie, Huizhe Zhang +4
The paper introduces Grad2Fair, a method that uses gradient information to detect and reduce group bias in graph neural networks without requiring demographic attributes, achieving…
MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model
Charilaos Papaioannou, Ioannis Tsantilas, Dimitris Giannakakos +9
The paper presents MxGPS, a multiplex graph transformer that jointly trains on static state estimation and AC power flow to avoid topology overfitting, achieving robust zero‑shot p…
NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis
Lincan Li, Zheng Chen, Yushun Dong
NeuroGRIP is a framework that refines EEG-based graph neural network predictions for seizure diagnosis by retrieving and integrating clinical knowledge from a domain-specific knowl…
OCELOT: Direct Atmospheric Forecasting from Heterogeneous Earth Observations Using a Graph-Transformer Hybrid Model
Azadeh Gholoubi, Ronald McLaren, Mu-Chieh Ko +7
The paper introduces OCELOT, a machine‑learning system that directly forecasts atmospheric observations up to 12 hours ahead by processing heterogeneous satellite and in‑situ data…
LATTICE: Graph Self-Supervised Learning for Multimodal Spatial Omics Integration
Jagan Mohan Reddy Dwarampudi, Veena Kochat, Suresh Satpati +3
LATTICE is a graph-based self‑supervised framework that learns spot‑level embeddings by integrating multimodal spatial omics data (RNA, ATAC, CUT&Tag) using a TransformerConv encod…