Graph Similarity
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Benchmarks
IMDb
Most implemented
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
GREED: A Neural Framework for Learning Graph Distance Functions
SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Papers
pro-team at LLMs4OL 2026 Tasks Flagship and Reuse: Retrieval-Augmented Generation and Vocabulary-Constrained Filtering for Ontology Learning
Ontology learning from text remains challenging despite significant progress in Large Language Models (LLMs), which can hallucinate domain terms, produce inconsistent formats, and favor hierarchical over associative rela…
Graph SimilarityTACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction
Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and reasoning. Cybersecurity Knowledge Graphs (CSKGs) provide a…
Graph SimilarityKnowledge GraphsClinical Reasoning Graphs: Structured Evaluation of LLM Diagnostic Reasoning Reveals Competence Without Consistency
Modern large language models (LLMs) reach 60-70% diagnostic accuracy on complex clinical case benchmarks, but accuracy alone cannot distinguish stable clinically-grounded reasoning from pattern matching. We introduce cli…
Graph SimilaritySpecification-Based Code-Text-Code Reengineering for LLM-Mediated Software Evolution
Direct Code2Code transformation remains challenging to control because it can preserve surface-level syntax while introducing semantic drift, hidden behavioral changes, loss of traceability, non-idiomatic target implemen…
Graph SimilarityTowards Metric-Faithful Neural Graph Matching
Graph Edit Distance (GED) is a fundamental, albeit NP-hard, metric for structural graph similarity. Recent neural graph matching architectures approximate GED by first encoding graphs with a Graph Neural Network (GNN) an…
Graph Neural NetworkGraph SimilarityGraph MatchingWhen Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors
Model distillation is a primary driver behind the rapid progress of LLM agents, yet it often leads to behavioral homogenization. Many emerging agents share nearly identical reasoning steps and failure modes, suggesting t…
Graph Similarity