{"total":16,"items":[{"citing_arxiv_id":"2607.01205","ref_index":188,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Linkify: Learning from Interface-Augmented Assembly Graphs","primary_cat":"cs.CV","submitted_at":"2026-07-01T17:45:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Linkify augments assembly graphs with corrected interface point clouds and trains GATv2 for masked part prediction, outperforming non-graph baselines on Fusion 360 data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.26183","ref_index":10,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"What Molecular Structure Cannot Tell Us: A Taxonomy of Explainability Gaps in GNN-Based Drug Toxicity Prediction","primary_cat":"q-bio.QM","submitted_at":"2026-05-25T07:51:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces a four-category taxonomy of structural explainability gaps in GNN drug toxicity prediction, with a case study on Aspirin indicating molecular structure accounts for 5 of 11 known adverse effects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22963","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Graph Alignment Topology as an Inductive Bias for Grounding Detection","primary_cat":"cs.CL","submitted_at":"2026-05-21T18:49:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A GNN trained on bipartite alignment graphs between references and LLM generations reports state-of-the-art hallucination detection across four datasets, beating prior methods and GPT-4o.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01484","ref_index":82,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks","primary_cat":"cs.LG","submitted_at":"2026-05-02T15:11:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"due to the context length of LLMs. Instead, depend- ing on the task, we can summarize the statistics of the walks that can easily scale with the graph size. 4 Estimation of Graph Properties 4.1 Estimation of Number of Nodes and Edges Estimating the size of a graph and other proper- ties has been extensively studied in the graph lit- Graph description: [(149, 32), (145, 220), (126, 222), (15, 77), (190, 191), (223, 224), (18, 232), (137, 174), (18, 19), (247, 52), (157, 178), (11, 162), (160, 2), (174, 246), (114, 37), (120, 213), (132, 133), (5, 11), (3, 4), (142, 53), (24, 29), (105, 101), (5, 13), (112, 56), (31, 34), (165, 106), (32, 236), (220, 203), (230, 231), (143, 145), (17, 20), (35, 36), (158, 30), (14, 66), (89, 91), (156, 157), (95, 61), (135, 172), (112, 138), (161, 56), (123, 106), (147, 150), (145, 136), (198, 90), (18, 20), (202, 13), (33, 114), (39, 40), (143, 144), (82, 84), (5, 14), (169, 168), (119, 213), (240, 171), (121, 192), (239, 95), (126, 218), (44, 87), (197, 198), (240, 167), (43, 169), (18, 53), (89, 90), (0, 2), (98, 95), (207, 168), (5, 15), (114, 68), (47, 48), (92, 96), (74, 32), (216, 240), (67, 70), (46, 167), (156, 158), (132, 136), (55, 245), (155, 168), (133, 2), (32, 227) …+Task description Random Walks RW-1 description …RW-2 description …RW-3 description …RW-4 description …Task description+ [ ] [ ] Running out of context length LLM full graph description Graph description within context length Figure 2: Figure illustrates the issue of exceeding context length as the graph size increases. Random walks on graphs provide efficient way of extracting and encoding graph-related information. erature especially for social networks like Face- book or Twitter."},{"citing_arxiv_id":"2604.23514","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States","primary_cat":"stat.ML","submitted_at":"2026-04-26T03:22:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A probabilistic graphical model framework with graph neural network inference computes Bayesian posteriors for discrete structural states, claimed to match traditional Bayesian results while scaling to high-dimensional problems via topology-informed learning and scale-adaptive 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Entanglement","primary_cat":"cs.CR","submitted_at":"2026-04-15T09:02:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TopFeaRe models graph adversarial attacks as oscillations in a complex dynamic system and locates the critical resilience state via equilibrium-point theory applied to a two-dimensional topology-feature entangled function.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.07520","ref_index":27,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"On the Effectiveness of Code Representation in Deep Learning-Based Automated Patch Correctness Assessment","primary_cat":"cs.SE","submitted_at":"2026-03-08T08:18:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Graph-based code representations such as Code Property Graphs achieve the highest accuracy (average 82.6%) in predicting patch correctness across 15 benchmarks and outperform sequence and tree representations when used with GNN classifiers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.07243","ref_index":17,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Beyond the Edge of Function: Unraveling the Patterns of Type Recovery in Binary Code","primary_cat":"cs.CR","submitted_at":"2025-03-10T12:27:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ByteTR recovers variable types in binary code more effectively than prior methods by decoupling unbalanced type sets, mitigating compiler optimization effects via static analysis, and modeling inter-procedural data flows with a gated GNN.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2409.08036","ref_index":27,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Heterogeneous Sheaf Neural Networks","primary_cat":"cs.LG","submitted_at":"2024-09-12T13:38:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HetSheaf applies cellular sheaves and type-conditioned restriction maps to heterogeneous graphs, plus SheafPool for basis-invariant graph-level representations, delivering competitive accuracy with substantially reduced parameter counts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2304.10726","ref_index":46,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts","primary_cat":"cs.CR","submitted_at":"2023-04-21T03:45:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DLVA trains neural networks on bytecode to match Slither source labels at 92.7% accuracy and 0.2 seconds per contract while outperforming nine other tools at 99.7% average accuracy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2104.13478","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","primary_cat":"cs.LG","submitted_at":"2021-04-27T21:09:51+00:00","verdict":"ACCEPT","verdict_confidence":"HIGH","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2003.03485","ref_index":58,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","primary_cat":"cs.LG","submitted_at":"2020-03-07T01:56:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Graph Kernel Networks learn PDE solution operators that generalize across discretization methods and grid resolutions using graph-based kernel integration.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1908.05387","ref_index":50,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"HONEM: Learning Embedding for Higher Order 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