Dynamic Graph Learning

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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DevoTG: Temporal Graph Neural Networks for Modeling C. elegans Developmental Connectomics
Jun 20, 2026Jayadratha Gayen, Bradly AliceaTemporal GNNs Dynamic Graph Learning
1IIIT Hyderabad · 2OpenWorm Foundation · 3Orthogonal Research and Education Laboratory
TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction
Jun 15, 2026Bohao Liao, Boyu Deng, Qipeng Song +2Temporal GNNs Heterogeneous GNNs
School of Telecommunication Engineering, Xidian University, Xi’an, 710071, Shaanxi, China · National Key Laboratory of Multi-domain Data Collaborative Processing and Control, Beijing, 100141, China · School of Cyber Engineering, Xidian University, Xi’an, 710071, Shaanxi, China
Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport
Jun 8, 2026Md Sadek Hossain Asif, Tanzila Khan, Md. Mosaddek KhanTemporal GNNs Graph Neural Networks
University of Dhaka
Geodesics of Dynamic Graphs for Regime Change Detection
Jun 5, 2026William Cappelletti, Étienne Voutaz, Pascal FrossardDynamic Graph Learning Change-Point Detection
LTS4, EPFL, 1015 Lausanne, Switzerland · Cyber-Defence Campus, Armasuisse, 3604 Thun, Switzerland
Zero-Copy Semantic Contagion: An In-Memory Streaming Architecture for Evolving Attention Graphs
Jun 4, 2026Kabir MurjaniDynamic Graph Learning Financial Time Series
Department of Electrical Engineering Nirma University Ahmedabad, India
HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery
Jun 4, 2026Phillip JiangHeterogeneous GNNs Visual Object Tracking
Appsofa LLC
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
Jun 3, 2026Ayushman Raghuvanshi, Thummaluru Siddartha Reddy, Sundeep Prabhakar Chepuri +1Long-Context Modeling Temporal Link Prediction
Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore
Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network
Jun 3, 2026Yaqian Zhan, Jialan He, Tianzhu ChenDifferential Evolution Graph Representation Learning
College of Computer and Information Science Southwest University Chongqing, China
SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale
Jun 2, 2026Tong Bai, Zhenglin Wan, Pengfei Zhou +3LLM Agent Skill Retrieval Dynamic Graph Learning
1Fudan University · 2National University of Singapore · 3CFAR, A*STAR
DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
May 29, 2026Ashley Hoi-Ting Au, Zikun Zhang, Ligang He +1Communication-Efficient Distributed Training Federated Graph Learning
Department of Computer Science, The University of Warwick · School of Computing and Communications, Lancaster University
From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks
May 28, 2026Du Yin, Hao Xue, Arian Prabowo +2Intelligent Transportation Systems Forecasting Benchmarks
University of New South Wales, Sydney, NSW, Australia
Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
May 28, 2026Qian Chang, Ciprian Doru Giurcaneanu, Runsong Jia +6Temporal GNNs Graph Representation Learning
University of Auckland, Auckland, New Zealand · University of Technology Sydney, Sydney, Australia · Central China Normal University, Wuhan, China
Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification
May 28, 2026Aydin Wells, Francis A. Gatsi, Aaron Striegel +1Protein Structure Prediction Graph Representation Learning
Department of Computer Science and Engineering, University of Notre Dame, USA
GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study
May 27, 2026Gaspard Michel, Elena V. Epure, Romain Hennequin +2Computational Literary Studies Heterogeneous GNNs
Deezer Research, Paris, France · IDIAP, Martigny, Switzerland · Loria, Nancy, France +1
Learning Dynamic Graph Representations through Timespan View Contrasts
May 26, 2026Yiming Xu, Zhen Peng, Bin Shi +2Graph SSL Dynamic Graph Learning
School of Computer Science and Technology, Xi’an Jiaotong University, P.R. China · School of Distance Education, Xi’an Jiaotong University, P.R. China
SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning
May 25, 2026Shubhajit Roy, Anirban DasguptaGraph Representation Learning Temporal Link Prediction
Department of Computer Science and Engineering Indian Institute of Technology Gandhinagar
Evolving Causal Regulatory Networks (ECR-Net)
May 24, 2026Govind Vallabhasseri Binish, Abdhul Ahadh, Rano Roy Kavanal +1Structural Causal Models Causal Discovery
Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
May 22, 2026Yunhua Pei, Zixing Song, Jin Zheng +1Dynamic Graph Learning Unsupervised Anomaly Detection
School of Computer Science, University of Bristol, Bristol, UK · School of Engineering Mathematics, University of Bristol, Bristol, UK
Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularization
May 18, 2026Chuansen Peng, Xiaojing ShenRiemannian Optimization Dynamic Graph Learning
School of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, China
Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities
May 16, 2026Sha Tao, Jiao Pan, Yu Guo +1Missing-Modality Learning Heterogeneous GNNs
University of Science and Technology Beijing, Beijing, China
Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix
May 15, 2026Jinhao Zhang, Kangfei Zhao, Qiuhao Zeng +1Distribution Shift Robustness Temporal GNNs
Beijing Institute of Technology · University of Toronto · Hong Kong Baptist University
Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
May 13, 2026Haonan Yuan, Qingyun Sun, Junhua Shi +3Domain-Adaptive Pretraining Prompt Tuning
Can Graphs Help Vision SSMs See Better?
May 11, 2026Dhruv Parikh, Anvitha Ramachandran, Haoyang Fan +3Graph Structure Learning Graph Neural Networks
USC · UT Austin · DEVCOM ARL Army Research Office, USA
CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
May 7, 2026Mei Wu, Wenchao Weng, Wenxin Su +2Contrastive Learning Traffic Flow Estimation
College of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China · School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
CHE-TKG: Collaborative Historical Evidence and Evolutionary Dynamics Learning for Temporal Knowledge Graph Reasoning
May 6, 2026Shuai-long Lei, Xiaobin Zhu, Jiarui Liang +3Temporal Link Prediction Dynamic Graph Learning
University of Science and Technology Beijing
Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points
May 6, 2026Haruka Ezoe, Ryohei HisanoDynamic Graph Learning Change-Point Detection
Graduate School of Information Science and Technology The University of Tokyo · The Canon Institute for Global Studies
Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs
May 5, 2026Vincenzo Marco De Luca, Antonio Longa, Giovanna Varni +1Clinical Outcome Prediction Dynamic Graph Learning
a University of Trento · b UiT the Arctic University of Norway
Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
May 2, 2026Aadarsh Senapati, Neha Kujur, Vivek YelletiDynamic Graph Learning Recommender Systems
Department of Computer Science and Engineering, SRM University Andhra Pradesh, Mangalagiri, Andhra Pradesh 522502, India
TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
Apr 29, 2026Wei Yang, Rui Zhong, Zihan Lin +4Dynamic Graph Learning Recommender Systems
Xiaohongshu Inc. · Beijing, China
Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning
Apr 27, 2026Xiaoyi Wang, Zhiqiang Wang, Jianqing Liang +4Temporal GNNs Graph Representation Learning
Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China. · Department of System Engineering and Engineering Management, City University of Hong Kong, Kowloon 999077, Hong Kong, China. · Key Laboratory of Complex Systems and Data Science of Ministry of Education, Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China.

Jun 20, 2026, cs.LGJ/K move · Enter open · S save

DevoTG: Temporal Graph Neural Networks for Modeling C. elegans Developmental Connectomics

Jayadratha Gayen, Bradly Alicea

1IIIT Hyderabad · 2OpenWorm Foundation · 3Orthogonal Research and Education Laboratory

Temporal GNNs Dynamic Graph Learning

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Understanding how a nervous system wires itself from birth to adulthood is a fundamental challenge in developmental neuroscience. We present DevoTG, a temporal graph framework that applies Temporal Graph Neural Networks (TGNs) to two complementary representations of C. elegans neural development: a Continuous-Time Dynamic Graph (CTDG) of cell division events derived from cell lineage data, and a Discrete-Time Dynamic Graph (DTDG) of the developing synaptic connectome spanning eight reconstructed electron-microscopy datasets. On the lineage prediction task, our TGN achieves a mean test AUC of 0.839 +/- 0.007 (5 seeds; validation AUC 0.937 +/- 0.001), outperforming a static GNN with the identical architecture by 26 AUC points (0.577 +/- 0.080), demonstrating that temporal memory is the decisive factor. Applied to the connectome DTDG, DevoTG identifies three connection stability classes (stable, developmental, and variable) across 225 neurons and 858 to 2,496 connections over development (L1 birth to adult), providing a temporal-graph-theoretic complement to the individual-variability classification of Witvliet et al. Analysis of hub command interneurons AVA, AVB, and AVE reveals their persistent centrality and how their integration roles are progressively reinforced across larval stages. Accompanying interactive visualizations (3D animated networks, centrality heatmaps, and a spatiotemporal lineage graph) make developmental dynamics accessible for biological hypothesis generation. DevoTG is open-source and designed for extension to other developing nervous systems. Code is publicly available at https://github.com/DevoLearn/DevoGraph/tree/main/DevoTG.

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