Temporal Link Prediction

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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
A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
May 21, 2026Nouhaila Innan, M. Murali Karthick, Simeon Kandan Sonar +2Temporal GNNs Quantum Machine Learning
1eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · 3Indian Institute of Technology Palakkad (IITPKD), Palakkad 678623, India · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE
Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning
May 13, 2026Rikui Huang, Shengzhe Zhang, Wei WeiReasoning Evaluation Temporal Link Prediction
School of Computer Science & Technology, Huazhong University of Science and Technology · Institute of Artificial Intelligence, Huazhong University of Science and Technology · School of Artificial Intelligence & Automation, Huazhong University of Science and Technology
PRISM: Iterative Cross-Modal Posterior Refinement for Dynamic Text-Attributed Graphs
May 7, 2026Trimble Chang, Yihang Liu, Mingjing Han +1Cross-Modal Representation Learning Temporal Link Prediction
College of Artificial Intelligence Nankai University
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
H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction
May 4, 2026Zhexi Gu, Jiaxin Ying, Xu-Wen Wang +1Healthcare Link Prediction
School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA · Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA · School of Data Science and Society, the Department of Mathematics, and the Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation
Apr 7, 2026Dongying Lin, Yinan Liu, Shengwei tang +2KG Embedding Temporal Link Prediction
Northeastern University, Shenyang, China
A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
Feb 15, 2026Nafiseh Sadat Sajadi, Behnam Bahrak, Mahdi Jafari SiavoshaniTemporal GNNs Link Prediction
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. · Tehran Institute for Advanced Studies, Khatam University, Tehran, Iran.
What Do Temporal Graph Learning Models Learn?
Oct 10, 2025Abigail J. Hayes, Tobias Schumacher, Markus StrohmaierTemporal GNNs Temporal Link Prediction
University of Mannheim · RWTH Aachen University · GESIS - Leibniz Institute for the Social Sciences +1
Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
Aug 20, 2025Jiafeng Xiong, Rizos SakellariouGraph Structure Learning Temporal GNNs
Department of Computer Science, University of Manchester, UK
BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Aug 9, 2025Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends +1Functional Connectivity Temporal GNNs
New Jersey Institute of Technology · University of Oxford
Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision
May 18, 2025Alexander Panyshev, Dmitry Vinichenko, Oleg Travkin +2Temporal GNNs Temporal Link Prediction
Risk AI Research Lab Moscow Independent Research Institute of Artificial Intelligence Moscow, Russia · Risk AI Research Lab Moscow, Russia · Risk AI Research Lab Applied AI Institute Moscow, Russia
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
Apr 9, 2025Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb +2Temporal GNNs Temporal Link Prediction
Ben-Gurion University of the Negev, Israel · Purdue University, USA · University of Cambridge, UK

May 25, 2026, cs.LGJ/K move · Enter open · S save

SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning

Shubhajit Roy, Anirban Dasgupta

Department of Computer Science and Engineering Indian Institute of Technology Gandhinagar

Graph Representation Learning Temporal Link Prediction Dynamic Graph Learning Node Classification Spatiotemporal GNNs

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Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \emph{Temporal-first} approaches build per-node temporal embeddings and only afterward perform spatial aggregation, whereas \emph{Spatial-first} approaches invert this order, feeding the output of a graph convolution into a downstream temporal module. In either case, the rigid sequencing forces the second stage to consume an already-compressed summary produced by the first, ruling out joint reasoning over topology and evolution; effectively, the message-passing operator never gets to weight a neighbor's contribution by that neighbor's \emph{past} trajectory. This paper introduces \textbf{SiST-GNN} (\textbf{Si}multaneous \textbf{S}patial-\textbf{T}emporal \textbf{GNN}), which fuses the two signals inside a single message-passing operation rather than chaining them. At each snapshot, we maintain a recurrent hidden state per node that summarises its history, pairs it with the node's current feature vector, and treats the pair as two nodes joined by a cross-time edge; running a standard graph convolution on this temporally augmented graph yields the updated representation. We compare against fourteen link-prediction baselines under both the fixed-split and live-update evaluation regimes, and eleven baselines on node classification. Across the public benchmarks, SiST-GNN improves on the strongest prior method in link prediction by 1-18% in the fixed-split setting, and is the leading learned method on five of six datasets in the live-update setting, improving on the strongest prior method by 1-158% there. We additionally derive three dynamic node-classification tasks by discretizing the underlying continuous-time event streams; here SiST-GNN beats the leading discrete-time (DTDG) baseline by 7-23% and matches continuous-time (CTDG) methods that consume the raw events directly.

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