Hierarchical GNNs

GNN: Graph Neural Network

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10 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents
Oct 15, 2026Jaeyoung Lee, Joonwon Jang, Misuk KimHierarchical GNNs Factual Consistency Evaluation
Hanyang University · POSTECH
Hierarchical GNNs for power flow: letting physics shape the hierarchy
Sep 22, 2026Carmine Delle Femine, Leire Garin Atxaga, Asier Diaz-Iglesias +4Neural Surrogate Modeling Graph Neural Networks
Vicomtech Foundation, Department of Energy, Basque Research and Technology Alliance (BRTA), Donostia/San Sebastián, Spain · Department of Languages and Information Systems, University of the Basque Country (UPV/EHU), Donostia/San Sebastián, Spain
HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design
Sep 2, 2026Ge Sun, Gervasio Zaldivar, Yuan Tian +5Hierarchical GNNs Polymer Property Prediction
Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, USA · Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA · Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA +3
Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
Jun 24, 2026Yoonsik Hong, Diego KlabjanQuantitative Finance Hierarchical GNNs
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, 60208, IL, USA
Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis
Jun 3, 2026Yuantu Luo, Jun Tao, Linxiao Yu +1Graph Neural Networks Hierarchical GNNs
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China · Purple Mountain Laboratories, Nanjing 210096, China · Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), MoE, Nanjing 211189, Jiangsu, China +1
Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
May 8, 2026Md Sazzad Hossen, Avimanyu SahooGraph Heterophily Over-Squashing
University of Alabama in Huntsville, Huntsville, AL, USA.
PPI-Net connects molecular protein interactions to functional processes in disease
May 8, 2026Kyle Higgins, Guadalupe Gonzalez, Dennis Veselkov +2Cancer Genomics Graph Neural Networks
Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK. · Prescient Design, Genentech, South San Francisco, CA, USA. · Department of Computing, Imperial College London, U.K.
Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping
May 8, 2026Han Jang, Junhyeok Lee, Heeseong Eum +4Graph Neural Networks Hierarchical GNNs
Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea · Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine, Seoul, South Korea · Dept. of Biomedical Sciences, Seoul National University, Seoul, South Korea +4
Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks
Mar 11, 2026Mohamad Alkadamani, Amir Ghasemi, Halim YanikomerogluHierarchical GNNs Wireless Communications
Communications Research Centre Canada, Ottawa, ON, Canada · Carleton University, Ottawa, ON, Canada
Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
Feb 26, 2026Bin Xu, Yufei Zhou, Boling Song +6Hierarchical GNNs Cancer Survival Prediction
Nanjing University of Information Science and Technology, Nanjing, China · Case Western Reserve University, Cleveland, OH, USA · Georgia Institute of Technology and Emory University, Atlanta, GA, USA +4

Oct 15, 2026J/K move · Enter open · S save

Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents

Jaeyoung Lee, Joonwon Jang, Misuk Kim

Hanyang University · POSTECH

Hierarchical GNNs Factual Consistency Evaluation Graph Neural Networks

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Consistency within a document is a crucial feature indicative of its quality. Recently, within the vast amount of information produced across various media, there exists a significant number of low-quality documents that either lack internal consistency or contain content utterly unrelated to their headlines. Such low-quality documents induce fatigue in readers and undermine the credibility of the media source that provided them. Consequently, research to automatically detect these low-quality documents based on natural language processing is imperative. In this study, we introduce a hierarchical graph convolutional network (HGCN) that can detect internal inconsistencies within a document and incongruences between the title and body. Moreover, we constructed the Inconsistency Dataset, leveraging published news data and its meta-data, to train our model to detect document inconsistencies. Experimental results demonstrated that the HGCN achieved superior performance with an accuracy of 91.20% on our constructedInconsistency Dataset, outperforming other comparative models. Additionally, on the publicly available incongruent-related dataset, the proposed methodology demonstrated a performance of 92.00%, validating its general applicability. Finally, an ablation study further confirmed the significant impact of meta-data utilization on performance enhancement. We anticipate that our model can be universally applied to detect and filter low-quality documents in the real world.