Node Classification

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9 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Latest papers 52

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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
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
May 27, 2026Safal Thapaliya, Jiatan Huang, Chuxu ZhangLLM-Assisted Annotation Text-Attributed Graph Learning
University of Connecticut, USA
Closed-Form Node Classification with Exact Graph Unlearning
May 25, 2026Aditya Gaur, Charu SharmaGraph Representation Learning Privacy-Preserving ML
Machine Learning Lab · IIIT Hyderabad
ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification
May 25, 2026VSS Tejaswi Abburi, Saurabh J. Shigwan, Nitin KumarGraph Neural Networks Unsupervised Representation Learning
Department of Computer Science and Engineering, Shiv Nadar Institute of Eminence Deemed to be University, Delhi NCR, India
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
Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
May 19, 2026Sujan Chakraborty, Saptarshi BejGraph Representation Learning Graph Contrastive Learning
School of Data Science, Indian Institute of Science Education and Research, Thiruvananthapuram, 695551, Kerala, India
Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
May 18, 2026Brown Zaz, Mar Gonzàlez I Català, Ferran Hernandez Caralt +2Graph Neural Networks Graph-Based Semi-Supervised Learning
University of Cambridge
Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification
May 12, 2026Kamel Abdous, Nairouz Mrabah, Mohamed BouguessaGraph Representation Learning Spectral Methods
Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada
A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
May 6, 2026Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2KG Construction Graph Neural Networks
Nantes University, LS2N, Nantes, 44300, France · National Institute of Informatics, Chiyoda-ku, Tokyo, 101-8430, Japan · Institute of Science Tokyo, Tokyo, 152-8550, Japan
HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
May 6, 2026Xinyi Li, Ming Li, Lu Bai +5Heterogeneous GNNs Node Classification
Zhejiang Normal University · Beijing Normal University · Central University of Finance and Economics +2
Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
May 2, 2026Yuxuan Xiao, Shengzhong ZhangMulti-Label Classification Graph Neural Networks
Nanjing University of Aeronautics and Astronautics
Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe
Apr 24, 2026Yuchen Xiong, Swee Keong Yeap, Zhen Hong BanGraph Signal Processing Node Classification
China-ASEAN College of Marine Sciences · Xiamen University Malaysia · Sepang 43900, Selangor, Malaysia +1
F\textsuperscript{2}LP-AP: Fast & Flexible Label Propagation with Adaptive Propagation Kernel
Apr 22, 2026Yutong Shen, Ruizhe Xia, Jingyi Liu +1Graph-Based Semi-Supervised Learning Semi-Supervised Learning
Beijing University of Technology
FOCAL-Attention for Heterogeneous Multi-Label Prediction
Apr 21, 2026Chenghao Zhang, Qingqing Long, Ludi Wang +3Graph Attention Networks Multi-Label Classification
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China
Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
Apr 21, 2026Jeongwhan Choi, Jongwoo Kim, Woosung Kang +1Synthetic Data Pretraining OOD Generalization
KAIST Daejeon, Republic of Korea
Bridge-Centered Metapath Classification Using R-GCN-VGAE for Disaster-Resilient Maintenance Decisions
Apr 20, 2026Takato YasunoNode Classification
Takato Yasuno
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Apr 19, 2026Ziqing Wang, Kaize DingLLM Alignment Graph SSL
Northwestern University
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
Sep 28, 2025Fanlong Zeng, Wensheng Gan, Kangjie Chen +1Class-Imbalanced Learning Graph Neural Networks
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · School of Cybersecurity, Tianjin University, Tianjin 300354, China · University of Illinois Chicago, Chicago, USA
GraphPFN: A Prior-Data Fitted Graph Foundation Model
Sep 25, 2025Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov +2Synthetic Data Pretraining In-Context Learning
1HSE University · 2Yandex Research
Turning Tabular Foundation Models into Graph Foundation Models
Aug 28, 2025Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov +2Graph Foundation Models Node Classification
HSE University · Yandex Research
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Mar 5, 2025Shujie Li, Yuxia Wu, Yuan Fang +1Text-Attributed Graph Learning Node Classification
Beijing University of Post and Telecommunication · Beijing, China · Singapore Management University +1
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sep 9, 2024Sitao Luan, Qincheng Lu, Chenqing Hua +3Graph Neural Networks Node Classification
Mila - Quebec Artiﬁcial Intelligence Institute · 1McGill University

May 28, 2026, cs.LGJ/K move · Enter open · S save

Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs

Qian Chang, Ciprian Doru Giurcaneanu, Runsong Jia, Xia Li+5

University of Auckland, Auckland, New Zealand · University of Technology Sydney, Sydney, Australia · Central China Normal University, Wuhan, China

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

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Representation learning on dynamic graphs requires capturing complex dependencies that evolve across both time and structure. Existing approaches typically adopt fixed temporal decay schemes or predetermined structural propagation depths, limiting their ability to generalize across graphs with diverse interaction frequencies and topological characteristics. We propose Dual-Scale Retentive Dynamics (DSRD), a unified framework that maintains a retentive representation state encoding both temporal memory and structural context. DSRD introduces two key components: (i) a retentive state with dual-scale adaptation that jointly models temporal dynamics and structural propagation within a single recurrent formulation, and (ii) adaptive decay kernels with learnable time-sensitivity parameters that automatically balance short-term responsiveness and long-term retention based on the underlying interaction patterns. We provide theoretical analysis establishing the equivalence between event-wise parallel aggregation and efficient recurrent state updates, as well as stability and boundedness guarantees for the learned dynamics. Extensive experiments on 14 real-world benchmarks demonstrate that DSRD consistently achieves state-of-the-art performance on both link prediction and node classification tasks, with strong generalization across transductive and inductive settings.

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