Hierarchical GNNs

GNN: Graph Neural Network

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15 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

GTFormer: A Syntax-Free Graph Network Alternative to Transformers for 8192 $\times$ Length Extrapolation in Linear Time
Oct 27, 2026Elisabeth FinkLong-Context Modeling Sequence Modeling
Middlesex University, Department of Computer Science The Burroughs, Hendon, London NW4 4BT, UK.
Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks
Oct 24, 2026Bohan Yoon, Hyejin JangMetal-Organic Frameworks Graph Pooling
Institute Multiscale Matter and Systems, Ewha Womans University, Seoul, Republic of Korea · Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Republic of Korea
HierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction
Oct 19, 2026Huangchao Xu, Baohua Zhang, Qian Liu +2Protein-Ligand Binding Affinity Prediction Drug Discovery
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China
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, Basque Research and Technology Alliance (BRTA), Department of Energy, 20009 Donostia/San Sebastián, Spain · University of the Basque Country (UPV/EHU), Department of Languages and Information Systems, 20018 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
Advanced Global Wildfire Activity Modeling with Hierarchical Graph ODE
Jan 4, 2026Fan Xu, Wei Gong, Hao Wu +6Wildfire Forecasting Spatiotemporal GNNs
University of Science and Technology of China, Hefei, Anhui, China · Tsinghua University, Beijing, China · Southwest Jiaotong University, Chengdu, Sichuan, China +4
HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data
Jul 30, 2025Fang Wang, Paolo Ceravolo, Ernesto DamianiTemporal GNNs Hierarchical GNNs
College of Computing and Mathematical Sciences, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates · Department of Computer Science, University of Milan, Via Festa del Perdono 7, 20122, Milano, MI, Italy

Oct 27, 2026J/K move · Enter open · S save

GTFormer: A Syntax-Free Graph Network Alternative to Transformers for 8192 $\times$ Length Extrapolation in Linear Time

Elisabeth Fink

Middlesex University, Department of Computer Science The Burroughs, Hendon, London NW4 4BT, UK.

Long-Context Modeling Sequence Modeling Graph Neural Networks Hierarchical GNNs

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Self-attention mechanisms have established the Transformer as the dominant backbone across natural language processing, source code intelligence, and automated theorem proving. However, all-to-all pairwise token comparison enforces a quadratic

\mathcal{O}(N^2)

compute and memory barrier alongside severe softmax attention dilution as sequence length

N

scales beyond the training horizon. Inspired by recent advances in algebraic graph learning (WPNet), this work introduces GTFormer, a syntax-free hierarchical Graph Neural Network (GNN) that replaces flat self-attention for general long-context sequence modeling. GTFormer organizes raw token streams into a bounded-degree hierarchy combining a balanced binary reduction tree (

\lceil \log_2 N \rceil

depth), horizontal boundary-bridging shift edges, random expander shortcuts, multi-channel convex highway gating, and a top-down broadcast pass. Across eight long-context code, log, and multi-hop retrieval benchmarks trained exclusively on sequences of length

N \le 512

, GTFormer extrapolates zero-shot to

N = 4{,}194{,}304

(

8192\times

) on a single commodity GPU in linear

\mathcal{O}(N)

memory, maintaining near-perfect accuracy where standard and RoPE Transformers suffer from out-of-distribution attention collapse and out-of-memory exhaustion.