Fast Graph Query Processing Algorithms Using Dynamic Programming

김현준 2020년
' Fast Graph Query Processing Algorithms Using Dynamic Programming' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 응용 물리
  • adaptive matching order
  • backtracking
  • dynamic programming
  • graphqueryprocessing
  • subgraph isomorphism
  • subgraph matching
  • subgraph query processing
  • subgraph search
  • supergraph search
  • 그래프 쿼리 프로세싱
  • 동적 매칭 순서
  • 동적 프로그래밍
  • 백트래킹
  • 부분 그래프 동형
  • 부분 그래프 매칭
  • 부분그래프 검색
  • 부분그래프 쿼리 프로세싱
  • 수퍼그래프 검색
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
4,713 0

0.0%

' Fast Graph Query Processing Algorithms Using Dynamic Programming' 의 참고문헌

  • ¡°SAGA : A Subgraph Matching Tool for Biological Graphs
    23 ( 2 ) :232 { 239 [2006]
  • ¡°GADDI : Distance Index based Subgraph Matching in Biological Networks
    pages 192 { 203 , [2009]
  • iGraph : a framework for comparisons of disk-based graph indexing techniques
    3 ( 1-2 ) :449 { 459 [2010]
  • gspan : Graph-based substructure pattern mining
    pages 721 { 724 , [2002]
  • [93] P. Yanardag and S. Vishwanathan. Deep graph kernels. In Proceedings of SIGKDD, pages 1365{1374, 2015.
  • [88] Wikipedia. Small-world network, 2020. https://en.wikipedia.org/wiki/Small-world network.
  • [87] Wikipedia. Raspberry ellagitannin, 2020. https://en.wikipedia.org/wiki/Raspberry ellagitannin.
  • [86] G. Weikum, G. Kasneci, M. Ramanath, and F. Suchanek. The Future of DB & IR. 2020.
  • [84] P. Velickovic, G.Cucurull, A.Casanova, A. Romero, P. Lio, and Y. Bengio. Graph attention networks. In Proceedings of the InternationalConference on Learning Representations, 2018.
    [2018]
  • [80] Y. Tian and J. M. Patel. Tale: A tool for approximate large graph matching. In Proceedings of the IEEE International Conference on Data Engi- neering, pages 963{972, 2008.
  • [7] M. Cannataro and P. H. Guzzi. Data management of protein interaction networks, volume 17. John Wiley & Sons, 2012.
  • [78] The National Institutes of Health (NIH). PubChem. https://pubchem.ncbi.nlm.nih.gov.
  • [77] The NationalCancer Institute (NCI). The NationalCancer Institute database. http://cactus.nci.nih.gov/download/nci/index.html.
  • [72] T. A. Snijders, P. E. Pattison, G. L. Robins, and M. S. Handcock. New specifications for exponential random graph models. Sociological methodology, 36(1):99{153, 2006.
    36 ( 1 ) :99 { 153 , [2006]
  • [6] X. Bresson and T. Laurent. Residual gated graph convnets. arXiv preprint arXiv:1711.07553, 2017.
  • [67] S. Sahu, A. Mhedhbi, S. Salihoglu, J. Lin, and M. T.  Ozsu. The ubiquity of large graphs and surprising challenges of graph processing. Proceedings of the International Conference on Very Large Data Bases, 11(4):420{431, 2017.
  • [58] A. Mhedhbi and S. Salihoglu. Optimizing subgraph queries by combining binary and worst-case optimal joins. Proceedings of the International Conference on Very Large Data Bases, 12(11):1692{1704, 2019.
  • [57] G. Mei, N. Xu, and S.Cuomo. Degree distribution of delaunay triangulations. arXiv preprint arXiv:1805.08063, 2018.
    [2018]
  • [56] B. D. McKay and A. Piperno. Practical graph isomorphism, II. Journal of Symbolic Computation, 60:94{112, 2014.
  • [50] J. Leskovec and A. Krevl. SNAP Datasets: Stanford large network dataset collection, 2014. http://snap.stanford.edu/data.
  • [41] T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations, 2017.
  • [36] N. Keriven and G. Peyre. Universal invariant and equivariant graph neural networks. In Advances in Neural Information Processing Systems, pages 7090{7099, 2019.
  • [27] K. Gouda and M. Hassaan. CSI GED: An ecient approach for graph edit similarity computation. In Proceedings of the IEEE International Conference on Data Engineering, pages 265{276. IEEE, 2016.
  • [25] M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., 1979.
  • [14] D. G. Corneil and C. C. Gotlieb. An ecient algorithm for graph isomorphism. Journal of the ACM (JACM), 17(1):51{64, 1970.
  • What is twitter , a social network or a news media ?
    pages 591 { 600 [2010]
  • Weisfeiler and leman go neural : Higher-order graph neural networks .
    volume 33 , pages 4602 { 4609 , [2019]
  • Weighted substructure mining for image analysis
    pages 1 { 8 [2007]
  • V. L. Nimgaonkar , C. E. Loscher , E. M. Bauer , and S. Chaparala . Schizophrenia interactome with 504 novel protein { protein interactions .
    2 ( 1 ) :1 { 10 , [2016]
  • Turbo ux : A fast continuous subgraph matching system for streaming graph data .
    pages 411 { 426 [2018]
  • Turbo iso : Towards ultrafast and robust subgraph isomorphism search in large graph databases
    pages 337 { 348 , [2013]
  • Towards graph containment search and indexing
    pages 926 { 937 , [2007]
  • The structure and function ofComplex networks
    45 ( 2 ) :167 { 256 [2003]
  • The graph neural network model .
    20 ( 1 ) :61 { 80 , [2008]
  • The LDBC social network benchmark : Interactive workload
    pages 619 { 630 , [2015]
  • The AIDS antiviral screen dataset
  • Taming subgraph isomorphism for rdf query processing .
    8 ( 11 ) , [2015]
  • Taming Verification Hardness : An Ecient Algorithm for Testing Subgraph Isomorphism
    1 ( 1 ) :364 { 375 [2008]
  • Superstructure searching algorithm for generic reaction retrieval .
    45 ( 5 ) :1214 { 1222 , [2005]
  • Strong simulation : Capturing topology in graph pattern matching
    39 ( 1 ) :4 [2014]
  • Sing : Subgraph search in non-homogeneous graphs
    11 ( 1 ) :96 [2010]
  • Similarity search on supergraphContainment
    pages 637 { 648 , [2010]
  • Simgnn : A neural network approach to fast graph similarityComputation .
    pages 384 { 392 [2019]
  • Scaling up subgraph query processing with ecient subgraph matching
    pages 220 { 231 , [2019]
  • Scaling queries over big RDF graphs with semantic hash partitioning
    6 ( 14 ) :1894 { 1905 , [2013]
  • Scalable supergraph search in large graph databases
    pages 157 { 168 [2016]
  • Scalable subgraph enumeration in MapReduce .
    8 ( 10 ) :974 { 985 [2015]
  • Scalable distributed subgraph enumeration .
    10 ( 3 ) :217 { 228 , [2016]
  • Provably powerful graph networks
    pages 2153 { 2164 , [2019]
  • Prefindex : An ecient supergraphContainment search technique
    pages 360 { 378 [2010]
  • Practical graph isomorphism
    [1981]
  • Performance and scalability of indexed subgraph query processing methods .
    8 ( 12 ) :1566 { 1577 , [2015]
  • Parallel subgraph listing in a large-scale graph
    pages 625 { 636 [2014]
  • On the equivalence between graph isomorphism testing and function approximation with gnns
    pages 15868 { 15876 [2019]
  • On graph query optimization in large networks
    3 ( 1 ) :340 { 351 [2010]
  • Multi-query optimization for subgraph isomorphism search
    10 ( 3 ) :121 { 132 [2016]
  • Mining and indexing graphs for supergraph search
    6 ( 10 ) :829 { 840 [2013]
  • Malware analysis with tree automata inference
    pages 116 { 131 [2011]
  • MSQIndex : A succinct index for fast graph similarity search .
    [2019]
  • J. Masci , and N. M. Kriege . Deep graph matching consensus
    [2020]
  • Inductive representation learning on large graphs
    pages 1024 { 1034 [2017]
  • Incremental graph pattern matching
    38 ( 3 ) :18:1 { 18:47 [2013]
  • IDAR : Fast supergraph search using DAG integration .
    13 ( 9 ) :1456 { 1468 , [2020]
  • I. G. Tanase , Y. Xia , L. Nai , and P. Boncz .
    distributed platforms . Proceedings of the International Conference
  • How to find communities online using social network analysis
  • How powerful are graph neural networks ?
    [2019]
  • Hierarchical graph representation learning with di erentiable pooling
    pages 4800 { 4810 [2018]
  • Graphs-at-a-time : Query language and access methods for graph databases
    pages 405 { 418 [2008]
  • Graphcache : a caching system for graph queries
    pages 13 { 24 [2017]
  • Graph similarity search with edit distance constraint in large graph databases
    pages 1595 { 1600 [2013]
  • Graph similarity search on large uncertain graph databases
    24 ( 2 ) :271 { 296 [2015]
  • Graph pattern matching revised for social network analysis
    pages 8 { 21 [2012]
  • Graph pattern matching : from intractable to polynomial time
    3 ( 1-2 ) :264 { 275 [2010]
  • Graph neural networks : A review of methods and applications
    [2018]
  • Graph indexing : a frequent structure-based approach
    pages 335 { 346 [2004]
  • Graph indexing : Tree+ delta > graph
    pages 938 { 949 , [2007]
  • Graph Isomorphism Algorithm for Matching Large Graphs
    26 ( 10 ) :1367 { 1372 [2004]
  • Grapes : A software for parallel searching on biological graphs targeting multi-core architectures
    8 ( 10 ) : e76911 [2013]
  • Gpu-accelerated subgraph enumeration on partitioned graphs
    pages 1067 { 1082 , [2020]
  • Geometric deep learning on graphs and manifolds using mixture model cnns
    pages 5115 { 5124 , [2017]
  • Fast subgraph query processing and subgraph matching
    [2020]
  • Fast subgraph matching on large graphs using graphics processors
    pages 299 { 315 [2015]
  • Fast graph query processing with a low-cost index .
    20 ( 4 ) :521 { 539 [2011]
  • Fast and robust distributed subgraph enumeration
    12 ( 11 ) :1344 { 1356 , [2019]
  • Exploiting vertex relationships in speeding up subgraph isomorphism over large graphs .
    8 ( 5 ) :617 { 628 [2015]
  • Evograph : an e ective and ecient graph upscaling method for preserving graph properties
    pages 2051 { 2059 [2018]
  • Enhancing graph database indexing by sux tree structure
    pages 195 { 203 [2010]
  • Ecient subgraph matching on billion node graphs .
    5 ( 9 ) :788 { 799 , [2012]
  • Ecient subgraph matching by postponing Cartesian products
    pages 1199 { 1214 , [2016]
  • Ecient processing of graph similarity queries with edit distance constraints .
    22 ( 6 ) :727 { 752 [2013]
  • Ecient probabilistic supergraph search over large uncertain graphs
    pages 809 { 818 [2014]
  • Ecient probabilistic supergraph search .
    28 ( 4 ) :965 { 978 , [2015]
  • Ecient parallel subgraph enumeration on a single machine
    pages 232 { 243 [2019]
  • Ecient graph similarity search over large graph databases
    27 ( 4 ) :964 { 978 [2014]
  • Ecient Subgraph Matching : Harmonizing Dynamic Programming , Adpative Matching Order , and Failing Set Together .
    pages 1429 { 1446 , 2019 .
  • Diversified top-k subgraph querying in a large graph
    pages 1167 { 1182 [2016]
  • Diversified top-k graph pattern matching
    6 ( 13 ) :1510 { 1521 [2013]
  • Distributed subgraph matching on timely data ow
    12 ( 10 ) :1099 { 1112 [2019]
  • Distributed graph simulation : Impossibility and possibility
    7 ( 12 ) :1083 { 1094 [2014]
  • Distributed evaluation of subgraph queries using worst-case optimal low-memory data ows
    11 ( 6 ) :691 { 704 , [2018]
  • CT-index : Fingerprint-based graph indexing combining cycles and trees .
    pages 1115 { 1126 , [2011]
  • An in-depth comparison of subgraph isomorphism algorithms in graph databases
    6 ( 2 ) :133 { 144 , [2012]
  • An algorithm for subgraph isomorphism
    23 ( 1 ) :31 { 42 [1976]
  • A survey of graph edit distance . Pattern Analysis and applications
    13 ( 1 ) :113 { 129 [2010]
  • A selectivity based approach to continuous pattern detection in streaming graphs
    pages 157 { 168 [2015]
  • A novel spectral coding in a large graph database
    pages 181 { 192 [2008]
  • A novel approach for ecient supergraph query processing on graph databases
    pages 204 { 215 , [2009]
  • A comprehensive survey on graph neural networks
    [2020]
  • A comparative study of subgraph matching isomorphic methods in social networks
    6:66621 { 66631 , [2018]