专题：Graph Theory and Algorithms

This cluster of papers focuses on graph matching, graph processing, and pattern recognition techniques using distributed computing and parallel algorithms. It covers topics such as subgraph isomorphism, large-scale graphs, graph analytics, and spectral techniques for graph analysis.
最新文献
Text2Cypher: Bridging Natural Language and Graph Databases

preprint Full Text OpenAlex

Exact Random Graph Matching with Multiple Graphs

article Full Text OpenAlex

Multi-topology contrastive graph representation learning

article Full Text OpenAlex

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

review Full Text OpenAlex

CTHTC: A Hybrid Architecture for Temporal Knowledge Graph Completion

article Full Text OpenAlex

GraphCSR: A Degree-Equalized CSR Format for Large-Scale Graph Processing

article Full Text OpenAlex

Late Breaking Results: Statistical Timing Graph Scheduling Algorithm for GPU Computation

article Full Text OpenAlex

iG-kway: Incremental k-way Graph Partitioning on GPU

article Full Text OpenAlex

A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications

article Full Text OpenAlex

Implementing Low-Latency Machine Learning Pipelines Using Directed Acyclic Graphs

article Full Text OpenAlex

近5年高被引文献
Introduction to Graph Theory

book-chapter Full Text OpenAlex 890 FWCI2.1437527

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

preprint Full Text OpenAlex 544 FWCI0

Graph Learning: A Survey

article Full Text OpenAlex 384 FWCI42.47217612

Do Transformers Really Perform Badly for Graph Representation

article Full Text OpenAlex 384 FWCI35.55971495

HGNN+: General Hypergraph Neural Networks

article Full Text OpenAlex 340 FWCI66.5715989

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

article Full Text OpenAlex 325 FWCI83.01893848

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

review Full Text OpenAlex 307 FWCI196.10501458

Trained models, code, result data, and Optuna study data from "Hybrid quantum or purely classical? Assessing the utility of quantum feature embeddings."

preprint Full Text OpenAlex 302 FWCI0

Heterogeneous Graph Structure Learning for Graph Neural Networks

article Full Text OpenAlex 260 FWCI25.75013841

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

review Full Text OpenAlex 254 FWCI27.37409358