Temporal Link Prediction

Follow this topic Ask about this topic

Temporal link prediction estimates future interactions or facts from evolving networks, helping forecast communication, recommendations, biological relations, and knowledge-graph events. Methods range from temporal graph neural networks with memory, attention, or state-space models to motif and structural features, temporal knowledge-graph embeddings, and event-process models. Recent work also tests lightweight alternatives such as smoothed counts, memorization, and activity signals, which can rival learned representations on some benchmarks. A central challenge is measuring generalization beyond repeated links and familiar entities, especially under sparse data, long time horizons, and shifts in network structure.

Latest papers

62 papers8 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Revisiting Temporal Link Prediction through Structural Proximity
Oct 28, 2026Yuzhang Fei, Yanping Zheng, Lu Yi +1Temporal Link Prediction
Renmin University of China
Learning the Past from the Future: Retrospective Reconstruction in Temporal Interaction Networks
Oct 23, 2026Amarnath RTemporal Link Prediction Learning to Rank
Department of Information Technology, United Institute of Technology, Coimbatore, India
From Complex Histories to Simple Predictors: Predictive Dimension in Dynamic Graph Learning
Oct 20, 2026Hantong Feng, Duxin Chen, Wenwu YuDynamic Graph Learning Predictive Representation Learning
School of Cyber Science and Engineering, Southeast University. · School of Mathematics, Southeast University. · Frontiers Science Center for Mobile Information Communication and Security.
Bridging Concepts: Multi-Agent Reasoning over Scientific Concept Graphs for High-Impact Research Prediction
Oct 18, 2026Donggyu Yoon, Minsung Hwang, Juneha Baek +2Knowledge Graphs LLM Reasoning with Graphs
School of Computing, KAIST · College of Business, KAIST · Department of AI Computing, KAIST
Learning Across Temporal Graph Representation Silos
Oct 14, 2026Minwoo Yu, Young-guk HaDynamic Graph Learning Temporal KGs
M. Yu and Y.-g. Ha are with Smart Computing Laboratory, Department of Computer Science & Engineering, Konkuk University, Seoul 05029, Republic of Korea.
Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context
Oct 14, 2026Bo Li, Yue Yang, Yujin Huang +6Dynamic Graph Learning World Model Learning
School of Information and Communication Technology, Griffith University, Australia · Maincode, Australia · School of Computing and Information Systems, University of Melbourne, Australia +2
Your Temporal Link Predictor Is Blind to Who Is Active: A Missing Factor That Transfers Across Models
Oct 4, 2026Ji Zhang, Zixin Liu, Yiran Ding +3Temporal Link Prediction Dynamic Graph Learning
Tsinghua University
HiTS-CL: A Continual Learning Framework for Long-Horizon Temporal Knowledge Graph Extrapolation
Sep 29, 2026Yansong Liu, Rui Liu, Yuan Zuo +6Temporal Link Prediction Replay-Based Continual Learning
Beihang University Beijing, China · Beijing University of Posts and Telecommunications Beijing, China · Hubei Engineering University Xiaogan, Hubei, China
Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters
Sep 28, 2026Lisi Qarkaxhija, Ingo ScholtesLink Prediction Temporal Link Prediction
Chair of Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science (CAIDAS) Julius-Maximilians-Universität Würzburg, DE
TemporalGraphLLM: Temporal Graph Neural Networks with Large Language Models for Dynamic Text-Attributed Graphs
Sep 25, 2026Moran Beladev, Or Eitan, Gilad Katz +1Text-Attributed Graph Learning LLMs for Graph Learning
Department of Information Systems Engineering Ben-Gurion University of the Negev, Israel
BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting
Sep 24, 2026Zeyan Li, Libing Chen, Shengda Zhuo +2Temporal Link Prediction Temporal KGs
Shanghai Jiao Tong University · University of Chicago · Jinan University
CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning
Sep 22, 2026PinHeng Zong, Ye YuanTemporal GNNs Temporal Link Prediction
College of Computer and Information Science, Southwest University, Chongqing, China
A Temporal Knowledge Graph for Music Festival Lineup Forecasting
Sep 21, 2026Julia Gastinger, Thilo Dieing, Christian Meilicke +1Temporal Link Prediction Temporal KGs
University of Mannheim, Mannheim, Germany
Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting
Sep 16, 2026Fred Sun, Jingze Wang, Minkun Xu +1AI-Assisted Scientific Research Temporal Link Prediction
Center for Brain-Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, China · Guangdong Institute of Intelligence Science and Technology, Zhuhai, China
EdgeReMIND: A Scalable, Top-Ranked Memorization Baseline for Temporal Multi-Relational Link Prediction
Sep 15, 2026Bryant PollardLink Prediction Temporal Link Prediction
Clemson University
TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs
Sep 8, 2026Longfei Ma, Zemin Liu, Fei WuTemporal GNNs Semantic Drift in Language Models
1Zhejiang University
Hakken: Predicting future discoveries to fill the gaps in today's knowledge
Sep 3, 2026Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez +21Scientific Hypothesis Generation AI-Assisted Scientific Research
SonyAI, Barcelona, Spain. · SonyAI, Zurich, Switzerland. · SonyAI, Tokyo, Japan. +4
SCoR: A Hierarchical Framework for Forecasting Relations Between Scientific Concepts
Aug 27, 2026Jingze Wang, Fred Sun, Shangqi GuoScientific Discovery Temporal Link Prediction
Center for Brain-Inspired Computing Research, Tsinghua University
HAWKEYE: Seeing One Layer Deeper -- A Cohesion-Aware Structural Channel for Temporal Link Prediction
Aug 24, 2026Jiacheng Ding, Xiaofei ZhangTemporal Link Prediction Temporal GNNs
Department of Computer Science The University of Memphis Memphis, Tennessee, USA
Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation
Aug 21, 2026Yanglei Gan, Peng He, Run Lin +3Temporal KGs Temporal Link Prediction
Southwest Minzu University, Chengdu, China · University of Electronic Science and Technology of China, Chengdu, China · Weixin Group, Tencent, Gunagzhou, China +1
FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs
Aug 11, 2026Jiaxin Pan, Mojtaba Nayyeri, Osama Mohammed +4KG Embedding Temporal Link Prediction
University of Stuttgart, Stuttgart, Germany · SAP SE, Germany · University of Southampton, Southampton, United Kingdom
Where Does Neural Advantage Arise in Continuous-Time Dynamic Graph Prediction?
Aug 7, 2026Minwoo Yu, Young-guk HaTemporal GNNs Temporal Link Prediction
Smart Computing Laboratory, Department of Computer Science & Engineering, Konkuk University, Seoul 05029, Republic of Korea
Dynamic Spectral Filtering for Temporal Graph Learning: Learning Evolving Propagation Operators
Jul 30, 2026Yan KongTemporal GNNs Temporal Link Prediction
Nanjing University of Information Science and Technology, China
What Softmax Throws Away: Mass-Aware Attention for Evidence Accumulation
Jul 24, 2026Minwoo Yu, Young-guk HaTemporal GNNs Temporal Link Prediction
Smart Computing Laboratory, Department of Computer Science & Engineering, Konkuk University, Seoul 05029, Republic of Korea.
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
Jul 16, 2026Chien-Liang Liu, Tsao-Lun ChenTemporal Link Prediction Sparse-Reward RL
Artificial Intelligence Research Center, Chang Gung University · 2Graduate Institute of Oral Biology, National Taiwan University · 3Electrical Engineering, National Taiwan University of Science and Technology
GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs
Jul 16, 2026Xiangni Tian, Kaixian Yu, Runpeng Dai +2Event Time Prediction Temporal Link Prediction
Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, China · Insilicom LLC, Peabody, MA, USA · University of North Carolina at Chapel Hill, North Carolina, USA
GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention
Jul 11, 2026Jiaxin Pan, Osama Mohammed, Daniel Hernández +1Temporal Link Prediction Graph Foundation Models
Analytic Computing, Institute for Artificial Intelligence, University of Stuttgart, Stuttgart, Germany · University of Southampton, Southampton, United Kingdom.
TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
Jul 10, 2026Refat Ishrak Hemel, Ehsan Hallaji, Roozbeh Razavi-FarFinancial Fraud Detection Temporal Link Prediction
Trustworthy and Secure AI Lab (TSAI Lab), Faculty of Computer Science, University of New Brunswick, Canada
Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation
Jul 10, 2026Konrad Özdemir, Julia Gastinger, Lukas Kirchdorfer +1Distribution Shift Temporal Link Prediction
Data and Web Science Group, University of Mannheim, Germany · SAP Signavio, Walldorf, Germany
Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text
Jul 9, 2026Bharathwaj Vijayakumar, Sahana K. VaradarajuGraph Structure Learning Temporal Link Prediction
Institutional Data and Analytics, Rowan University, NJ, USA · Information Resources & Technology, Rowan University, NJ, USA

Oct 28, 2026J/K move · Enter open · S save

Revisiting Temporal Link Prediction through Structural Proximity

Yuzhang Fei, Yanping Zheng, Lu Yi, Zhewei Wei

Renmin University of China

Temporal Link Prediction

Open paper AskPDF arXiv

Existing temporal link prediction methods typically rely on learned temporal representations to capture evolving interaction patterns. In this paper, we show that a classical structural proximity measure can match or outperform these methods without training or learned temporal sequence modeling. We revisit Personalized PageRank (PPR) on historical interaction graphs, using simple sliding-window retention or exponential time decay. Surprisingly, across 14 datasets, Time-decay PPR ranks first on nine and second on four among the compared methods, including datasets with few repeated edges. To understand this result, we examine the historical paths connecting source nodes to previously unseen destinations. We find that such paths contain useful predictive information, and that PPR outperforms a strong neural baseline on queries where historical structure clearly distinguishes the true destination from negative candidates. These results show that strong performance on current benchmarks does not necessarily require complex temporal models. More fundamentally, they raise the question of how much predictive value learned temporal modeling adds beyond historical structural proximity. Our findings motivate reassessing progress in temporal link prediction with strong classical baselines.