Papers Graph Similarity
“Graph Similarity” 태그가 달린 논문 127편 · 필터 해제
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 SimilarityInstruction set for the representation of graphs
We present IsalGraph, a method for representing the structure of any finite, simple graph as a compact string over a nine-character instruction alphabet. The encoding is executed by a small virtual machine comprising a s…
Language ModellingGraph SimilarityGraph GenerationGED-Consistent Disentanglement of Aligned and Unaligned Substructures for Graph Similarity Learning
Graph Similarity Computation (GSC) is a fundamental graph related task where Graph Edit Distance (GED) serves as a prevalent metric. GED is determined by an optimal alignment between a pair of graphs that partitions each…
Graph Neural NetworkGraph SimilarityHeterogeneous Attributed Graph Learning via Neighborhood-Aware Star Kernels
Attributed graphs, typically characterized by irregular topologies and a mix of numerical and categorical attributes, are ubiquitous in diverse domains such as social networks, bioinformatics, and cheminformatics. While …
Graph SimilarityGraph LearningGRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph tasks and domains. However, existing GF…
Graph ClassificationGraph SimilarityGraph2Region: Efficient Graph Similarity Learning with Structure and Scale Restoration
Graph similarity is critical in graph-related tasks such as graph retrieval, where metrics like maximum common subgraph (MCS) and graph edit distance (GED) are commonly used. However, exact computations of these metrics …
Graph SimilarityGraph EmbeddingLayoutGKN: Graph Similarity Learning of Floor Plans
Floor plans depict building layouts and are often represented as graphs to capture the underlying spatial relationships. Comparison of these graphs is critical for applications like search, clustering, and data visualiza…
Graph SimilarityGraph MatchingEvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction
Dynamic graph-level embedding aims to capture structural evolution in networks, which is essential for modeling real-world scenarios. However, existing methods face two critical yet under-explored issues: Structural Visi…
Representation LearningAnomaly DetectionGraph SimilarityRalts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors
Graph neural networks (GNNs) have been widely applied in safety-critical applications, such as financial and medical networks, in which compromised predictions may cause catastrophic consequences. While existing research…
Graph Neural NetworkGraph SimilarityPieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI
Accurate graph similarity is critical for knowledge transfer in VLSI design, enabling the reuse of prior solutions to reduce engineering effort and turnaround time. We propose Pieceformer, a scalable, self-supervised sim…
Graph SimilarityManagementTransfer LearningSCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
Despite the dominance of convolutional and transformer-based architectures in image-to-image retrieval, these models are prone to biases arising from low-level visual features, such as color. Recognizing the lack of sema…
counterfactualGraph GenerationGraph SimilarityImage Captioning+3Neural Graduated Assignment for Maximum Common Edge Subgraphs
The Maximum Common Edge Subgraph (MCES) problem is a crucial challenge with significant implications in domains such as biology and chemistry. Traditional approaches, which include transformations into max-clique and sea…
Graph SimilarityPNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection
LiDAR loop closure detection (LCD) is crucial for consistent Simultaneous Localization and Mapping (SLAM) but faces challenges in robustness and accuracy. Existing methods, including semantic graph approaches, often suff…
Graph AttentionGraph SimilarityLoop Closure DetectionSimultaneous Localization and MappingFlexible Graph Similarity Computation With A Proactive Optimization Strategy
Graph Edit Distance (GED) offers a principled and flexible measure of graph similarity, as it quantifies the minimum cost needed to transform one graph into another with customizable edit operation costs. Despite recent …
Graph SimilarityPoint Cloud Objective Quality: Benchmarking Features and Quality Evaluation
Full-reference point cloud objective metrics are currently providing very accurate representations of perceptual quality. These metrics are usually composed of a set of features that are somehow combined, resulting in a …
AttributeBenchmarkingfeature selectionGraph Similarity+1