Papers graph construction
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Efficiently Constructing Sparse Navigable Graphs
Graph-based nearest neighbor search methods have seen a surge of popularity in recent years, offering state-of-the-art performance across a wide variety of applications. Central to these methods is the task of constructi…
graph constructionNGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation
Graph generation plays a pivotal role across numerous domains, including molecular design and knowledge graph construction. Although existing methods achieve considerable success in generating realistic graphs, their int…
graph constructionGraph GenerationIrec: A Metacognitive Scaffolding for Self-Regulated Learning through Just-in-Time Insight Recall: A Conceptual Framework and System Prototype
The core challenge in learning has shifted from knowledge acquisition to effective Self-Regulated Learning (SRL): planning, monitoring, and reflecting on one's learning. Existing digital tools, however, inadequately supp…
graph constructionLarge Language ModelRetrievalCall Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Static analysis plays a key role in finding bugs, including security issues. A critical step in static analysis is building accurate call graphs that model function calls in a program. However, due to hard-to-analyze lan…
graph constructionLink PredictionCORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks
Human smuggling networks are increasingly adaptive and difficult to analyze. Legal case documents offer valuable insights but are unstructured, lexically dense, and filled with ambiguous or shifting references-posing cha…
coreference-resolutionCoreference Resolutiongraph constructionSemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue organization and disease heterogeneity. However, current analysis methods of…
BenchmarkingContrastive Learninggraph constructionGraph Learning+1MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks. However, existing Mas construction methods typically rely on manually crafte…
Allgraph constructionLarge Language ModelReinforcement Learning (RL)XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing Large Language Model (LLM)'s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an inter…
graph constructionLanguage ModelingLanguage ModellingLarge Language Model+3MAGNet: A Multi-Scale Attention-Guided Graph Fusion Network for DRC Violation Detection
Design rule checking (DRC) is of great significance for cost reduction and design efficiency improvement in integrated circuit (IC) designs. Machine-learning-based DRC has become an important approach in computer-aided d…
graph constructionGraph Neural NetworkGraph Neural Networks in Modern AI-aided Drug Discovery
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive…
Drug Discoverygraph constructionMeta-LearningMolecular Property Prediction+4Zero-Shot Open-Schema Entity Structure Discovery
Entity structure extraction, which aims to extract entities and their associated attribute-value structures from text, is an essential task for text understanding and knowledge graph construction. Existing methods based …
Attributegraph constructionHIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification
Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not been extensively explored, is the classification of glomeruli health as …
graph constructionGraph Neural Networkwhole slide imagesGraph Neural Networks for Jamming Source Localization
Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly underexplored. In this work, we introdu…
feature selectiongraph constructionGraph Neural NetworkGraph RegressionOptimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning
Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) results in complex systems with numerous hyperparameters that directly affect performance. While such systems are increasingly common in retrieval-augm…
Chunkinggraph constructionHyperparameter OptimizationKnowledge Graphs+2LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing
Knowledge Graphs (KGs) structure real-world entities and their relationships into triples, enhancing machine reasoning for various tasks. While domain-specific KGs offer substantial benefits, their manual construction is…
Dependency Parsinggraph constructionKnowledge GraphsSpecificityPoint or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings
We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable things and the semantic regions of uncountable stuff in computer-aided design (CAD) drawings composed of ve…
graph constructionAutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge tripl…
graph constructionKnowledge GraphsRetrieval Augmented Generation based Large Language Models for Causality Mining
Causality detection and mining are important tasks in information retrieval due to their enormous use in information extraction, and knowledge graph construction. To solve these tasks, in existing literature there exist …
graph constructionInformation RetrievalPrompt EngineeringRAG+2Model Editing with Graph-Based External Memory
Large language models (LLMs) have revolutionized natural language processing, yet their practical utility is often limited by persistent issues of hallucinations and outdated parametric knowledge. Although post-training …
graph constructionmodelModel EditingMA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition
Recognizing biomedical concepts in the text is vital for ontology refinement, knowledge graph construction, and concept relationship discovery. However, traditional concept recognition methods, relying on explicit mentio…
graph construction