Distributed graph pattern matching

Ma, Shuai; Cao, Yang; Huai, Jinpeng; Wo, Tianyu

doi:10.1145/2187836.2187963

Cited by 68 publications

(50 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the Amazon data set was experimented with in [24] and [25], the Youtube video data set was experimented with in [24], and the Web graph data set was experimented with in [25]. The following is a detailed description of the data sets.…”

Section: Experimental Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Query optimization of distributed pattern matching

Huang

Venkatraman

Abadi

2014

2014 IEEE 30th International Conference on Data Engineering

View full text Add to dashboard Cite

Abstract-Greedy algorithms for subgraph pattern matching operations are often sufficient when the graph data set can be held in memory on a single machine. However, as graph data sets increasingly expand and require external storage and partitioning across a cluster of machines, more sophisticated query optimization techniques become critical to avoid explosions in query latency. In this paper, we introduce several query optimization techniques for distributed graph pattern matching. These techniques include (1) a System-R style dynamic programming-based optimization algorithm that considers both linear and bushy plans, (2) a cycle detection-based algorithm that leverages cycles to reduce intermediate result set sizes, and (3) a computation reusing technique that eliminates redundant query execution and data transfer over the network. Experimental results show that these algorithms can lead to an order of magnitude improvement in query performance.

show abstract

Section: Experimental Evaluationmentioning

confidence: 99%

“…However, query optimization is not considered. [24] and [25] focus on pattern matching in the context of graph simulation. [32] and [38] are built on top of Trinity [7] and use graph exploration in query optimization for large vertex-labeled undirected graphs and RDF graphs, respectively.…”

Section: F Summary Of Experimental Findingsmentioning

confidence: 99%

Query optimization of distributed pattern matching

Huang

Venkatraman

Abadi

2014

2014 IEEE 30th International Conference on Data Engineering

View full text Add to dashboard Cite

show abstract

“…Lee et al give detailed comparisons of several well-known exact subgraph matching algorithms [20]. Algorithms for inexact subgraph matching problem are heuristic-based [17,18,22,27,28,39]. For example, TALE [28] by Tian et al first locates mappings of a subset of "important" nodes and then expands them to the whole graph.…”

Section: Related Workmentioning

confidence: 99%

Subgraph Search in Large Graphs with Result Diversification

Yu¹,

Yuan

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

The problem of subgraph search in large graphs has wide applications in both nature and social science. The subgraph search results are typically ordered based on graph similarity score. In this paper, we study the problem of ranking the subgraph search results based on diversification. We design two ranking measures based on both similarity and diversity, and formalize the problem as an optimization problem. We give two efficient algorithms, the greedy selection and the swapping selection with provable performance guarantee. We also propose a novel local search heuristic with at least 100 times speedup and a similar solution quality. We demonstrate the efficiency and effectiveness of our approaches via extensive experiments.

show abstract

“…Although the first practical distributed approach for graph simulation was introduced in [31], their approach is not based on a vertex-centric model and uses a modified version of the HHK [3] algorithm. Also, not all of its stages are run in parallel and one is strictly serial.The idea of dual and strong simulation were first introduced in [10].…”

Section: Related Workmentioning

confidence: 99%

Techniques for Graph Analytics on Big Data

Nisar

Fard

Miller

2013

2013 IEEE International Congress on Big Data

View full text Add to dashboard Cite

Abstract-Graphs enjoy profound importance because of their versatility and expressivity. They can be effectively used to represent social networks, web search engines and genome sequencing. The field of graph pattern matching has been of significant importance and has wide-spread applications. Conceptually, we want to find subgraphs that match a pattern in a given graph. Much work has been done in this field with solutions like Subgraph Isomorphism and Regular Expression matching. With Big Data, scientists are frequently running into massive graphs that have amplified the challenge that this area poses. We study the speedup and communication behavior of three distributed algorithms for inexact graph pattern matching. We also study the impact of different graph partitionings on runtime and network I/O. Our extensive results show that the algorithms exhibit excellent scalable behavior and min-cut partitioning can lead to improved performance under some circumstances, and can drastically reduce the network traffic as well.

show abstract

Distributed graph pattern matching

Cited by 68 publications

References 22 publications

Query optimization of distributed pattern matching

Query optimization of distributed pattern matching

Subgraph Search in Large Graphs with Result Diversification

Techniques for Graph Analytics on Big Data

Contact Info

Product

Resources

About