Graphs-at-a-time: query language and access methods for graph databases

By Huahai He, Ambuj K. Singh. SIGMOD Conference, 2008

528 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/05d39c8c8f8a04a328abb248a2b59808c2c20e31

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In Artificial Intelligence

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Citations in the corpus

Efficient Subgraph Matching on Billion Node Graphs (2012, methodology): “Even though local pruning strategies are used in [15] to reduce the search space, the repetitive traversals of the graph cannot be avoided and cannot be applied to parallel processing.”
An In-depth Comparison of Subgraph Isomorphism Algorithms in Graph Databases (2012, methodology): “For detailed explanation, we refer readers to [5].”
Similarity Search in Graph Databases: A Multi-Layered Indexing Approach (2017, background): “The proliferation of graphs has sparked a growing interest in enabling efficient access capabilities and flexible, structure-aware querying functionalities in large graph databases [18], [2], [16].”
On graph query optimization in large networks (2010, methodology): “Articles from this volume were presented at The 36th International Conference on Very Large Data Bases, September 13-17,”
GADDI: distance index based subgraph matching in biological networks (2009, methodology): “In [18], the authors introduced a pattern matching method based on a combination of techniques: use of neighborhood subgraphs and profiles, joint reduction of the search space, and optimization of the search order.”
A Distributed Graph Engine for Web Scale RDF Data (2013, background): “A related research area is subgraph matching [13, 40, 19, 39] but most solutions rely on complex indexing techniques that are often very costly, and do not have the scalability to process web scale RDF graphs.”
Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases (2013, methodology): “We stopped subgraph isomorphism search after the first 1,000 subgraph isomorphisms were found, as it was done in [6, 21, 8].”
GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph (2014, methodology): “For comparison, we have implemented GGQL; a modified version of G RAM I that replaces ISFREQUENTwith a frequency evaluation function based on G RAPHQL [16]; one of the fastest state-of-the-art subgraph isomorphism techniques [21].”