Graph Representations

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26 papers in the last 28 days · 0.4% of indexed attention

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Period ending 2026-09-21

10 new papers

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Period ending 2026-09-14

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200 papers

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When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift
Apr 21, 2026Saket MagantiGraph Neural Networks Fraud Detection
Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
Apr 21, 2026Jeongwhan Choi, Jongwoo Kim, Woosung Kang +1Graph Neural Networks Graph Representations
Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph
Apr 20, 2026Vassilios Exarhakos, Jinghui Cheng, Jin L. C. GuoAi-Ran Graph Representations
ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation
Apr 20, 2026Qingying Niu, Yuhao Wang, Ruiyang Ren +2Arbitration Factual Recall
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation
Apr 20, 2026Lin Mu, Guoji Wang, Li Ni +4Recommendation Low-Rank Adaptation
LoReC: Rethinking Large Language Models for Graph Data Analysis
Apr 20, 2026Hongyu Zhan, Qixin Wang, Yusen Tan +6Graph Representations Graph Language Models
LLM-Guided Strategy Synthesis for Scalable Equality Saturation
Apr 19, 2026Chenyun Yin, Youwei Xiao, Yuze Luo +2Satisfiability Code Optimization
VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs
Apr 16, 2026Sazzadul Islam, Tasnim Tabassum, Hao ZhengVhdl Hardware Description Languages
A Residual-Shell-Based Lower Bound for Ollivier-Ricci Curvature
Apr 14, 2026Xiang Gu, Huichun Zhang, Jian SunLower Bounds Wasserstein Distance
High-Dimensional Change Point Detection via Graph Spanning Ratio
Dec 8, 2025Katerina Papagiannouli, Yang-wen Sun, Vladimir SpokoinyChange-Point Detection Change Detection
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
Nov 14, 2025Brian Godwin Lim, Galvin Brice Lim, Renzo Roel Tan +2Graph Neural Networks Message Passing Neural Networks
OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling
Sep 28, 2025Jihu Guo, Tenghui Ma, Wei Gao +6Parallel Partitioning
GraphPFN: A Prior-Data Fitted Graph Foundation Model
Sep 25, 2025Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov +2Graph Foundation Model Graph Neural Networks
GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
Sep 17, 2025Savini Kashmira, Jayanaka Dantanarayana, Thamirawaran Sathiyalogeswaran +3Pytorch Heterogeneous Graph Transformers
Instance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture
Aug 11, 2025Xiang Li, Shanshan Wang, Chenglong XiaoMaximum Independent Set Graph Neural Networks
Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark
Aug 8, 2025Ngoc N. Tran, Anwar Said, Waseem Abbas +2Malware Detection Out-Of-Distribution Detection
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
Jun 12, 2025Hongyu Yao, Zijin Hong, Hao Chen +6Multimodal Recommendation Large-Scale Recommendation Systems
Causally Fair Node Classification on Non-IID Graph Data
May 3, 2025Yucong Dai, Lu Zhang, Yaowei Hu +2Fairness-Aware Learning Causal Graph
GraphChase: A Platform and Benchmark for Urban Network Security Games
Jan 29, 2025Shuxin Zhuang, Shuxin Li, Tianji Yang +4Graph Representations Classical Solvers
NervePool: A Simplicial Pooling Layer
May 10, 2023Sarah McGuire Scullen, Ernst Röell, Elizabeth Munch +2Product Spaces Graph Representations
Parametrized Power-Iteration Clustering for Directed Graphs
Oct 1, 2022Gwendal Debaussart-Joniec, Harry Sevi, Matthieu Jonckheere +1Image Clustering Graph Representations

Apr 21, 2026 · cs.LGJ/K move · Enter open · S save

When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift

Saket Maganti

The consensus that GCN, GraphSAGE, GAT, and EvolveGCN outperform feature-only baselines on the Elliptic Bitcoin Dataset is widely cited but has not been rigorously stress-tested under a leakage-free evaluation protocol. We perform a seed-matched inductive-versus-transductive comparison and find that this consensus does not hold. Under a strictly inductive protocol, Random Forest on raw features achieves F1 = 0.821 and outperforms all evaluated GNNs, while GraphSAGE reaches F1 = 0.689 +/- 0.017. A paired controlled experiment reveals a 39.5-point F1 gap attributable to training-time exposure to test-period adjacency. Additionally, edge-shuffle ablations show that randomly wired graphs outperform the real transaction graph, indicating that the dataset's topology can be misleading under temporal distribution shift. Hybrid models combining GNN embeddings with raw features provide only marginal gains and remain substantially below feature-only baselines. We release code, checkpoints, and a strict-inductive protocol to enable reproducible, leakage-free evaluation.

Graph Neural Networks Fraud Detection Graph Representations Transductive Active Labeling

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