Graph SSL

SSL: Self-Supervised Learning

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
May 22, 2026Jinyu Yang, Cheng Yang, Junze Chen +4Representation Learning Self-Supervised Pre-Training
Beijing University Of Posts and Telecommunications Beijing, China · Peng Cheng Laboratory Shenzhen, China · China Telecom Bestpay Beijing, China +1
Instance Discrimination for Link Prediction
May 18, 2026Valentin Cuzin-Rambaud, Mathieu Lefort, Rémy CazabetData Augmentation Link Prediction
SyCoSMA, DM2L, LIRIS, UCBL · Université Lyon 1, INSA Lyon, CNRS, LIRIS, UMR 5205, Lyon, France. · LIRIS, SyCoSMA, IRISA, MALT, UR +2
A Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions
May 12, 2026Mohamed Mahmoud Amar, Nairouz Mrabah, Mohamed Bouguessa +1Graph Representation Learning Graph SSL
Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada
Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
May 6, 2026Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2Graph SSL Text-Attributed Graph Learning
Nantes University, LS2N, Nantes, 44300, France. · National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku,2026 Tokyo 101-8430 Japan. · Institute of Science Tokyo, Tokyo 152-8550 Japan.
Adaptive Negative Scheduling for Graph Contrastive Learning
May 4, 2026Adnan Ali, Jinlong Li, Syed Muhammad Israr +1Graph Representation Learning Graph SSL
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
May 2, 2026Chuang Liu, Zelin Yao, Xueqi Ma +4Graph Representation Learning Self-Supervised Pre-Training
Sangfor Technologies Inc. · Wuhan University · The University of Melbourne +2
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Apr 19, 2026Ziqing Wang, Kaize DingLLM Alignment Graph SSL
Northwestern University
Graph self-supervised learning based on frequency corruption
Apr 17, 2026Haojie Li, Mengjiao Zhang, Guanfeng Liu +3Graph Neural Networks Graph Representation Learning
School of Data Science, Qingdao University of Science and Technology Qingdao, China · School of Information Science and Technology, Qingdao University of Science and Technology Qingdao, China · School of Computing, Macquarie University Sydney, Australia
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
Sep 29, 2025Yunhao Liang, Pujun Zhang, Yuan Qu +3Graph SSL Combinatorial Optimization
Faculty of Engineering, The University of Hong Kong, Hong Kong SAR, China · Costello College of Business, George Mason University, VA, USA · Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China +1
Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network
Nov 1, 2023Hongrui Gao, Yawen Li, Meiyu Liang +2Representation Learning Graph Representation Learning
Beijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications · School of Economics and Management, Beijing University of Posts and Telecommunications

May 22, 2026, cs.LGJ/K move · Enter open · S save

RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases

Jinyu Yang, Cheng Yang, Junze Chen, Zedi Liu+3

Beijing University Of Posts and Telecommunications Beijing, China · Peng Cheng Laboratory Shenzhen, China · China Telecom Bestpay Beijing, China+1

Representation Learning Self-Supervised Pre-Training Graph SSL Relational Deep Learning Relational Databases

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Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) methods enable end-to-end prediction by converting RDBs into graphs, where rows are represented as nodes and inter-table interactions are represented as edges, and then applying graph-based models for representation learning. Despite the strong capability of RDL, effective self-supervised pre-training for RDBs remains non-trivial. RDB tasks often require multi-faceted information across different perspectives and granularities. For example, user churn classification may rely more on interaction patterns, whereas consumption value prediction requires both user-item behaviors and intrinsic user attributes for fine-grained regression. Such heterogeneous needs challenge RDB representation learning, as pre-training objectives should cover comprehensive information for downstream adaptation. However, existing SSL methods typically derive supervision from a single facet, such as node-level intrinsic attributes or subgraph-level relational structures, providing limited adaptability. To this end, we propose RelPrism, a multi-faceted self-supervised learning framework for RDBs. RelPrism constructs intrinsic, relational, and hybrid attributes from distinct perspectives, and applies multi-granularity clustering to each perspective to form corresponding pseudo-task pools. Pre-training over these pools exposes representations to broader perspectives and granularity levels, yielding a stronger basis for downstream adaptation. Experiments on 14 tasks across 5 real-world datasets show that RelPrism improves ROC-AUC by 4.15% for classification and reduces MAE by 10.75% for regression over state-of-the-art baselines. Our code is available at https://anonymous.4open.science/r/RelPrism.

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