Efficient subgraph matching on billion node graphs

Sun, Zhao; Wang, Hongzhi; Wang, Haixun; Shao, Bin; Li, Jianzhong

doi:10.14778/2311906.2311907

Cited by 296 publications

(202 citation statements)

References 28 publications

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“…To deal with large graphs, Sun et al [25] recently introduced a parallel and distributed algorithm (which we call STW in this paper), in which they decompose the query graphs into 2-level trees, and apply graph exploration and a joint strategy to obtain solutions in a parallel manner over a distributed memory cloud. Unlike STW, our method uses GPUs to preserve the advantages of parallelism during computation, while simultaneously avoiding high communication costs between participating machines.…”

Section: Subgraph Matching Algorithmsmentioning

confidence: 99%

“…It first picks a query node which potentially contributes to minimizing the sizes of candidate sets of query nodes and edges. The number of candidates at the beginning is unknown, so we can estimate it using a node ranking function f (u) = deg(u) f req(u.label) [10,25], where deg(u) is the degree of a query node u and f req(u.label) is the number of data nodes having the same label as u. The score function prefers lower frequencies and higher degrees.…”

Section: Gpu-based Subgraph Matchingmentioning

confidence: 99%

See 1 more Smart Citation

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching

2016

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Abstract. Common-sense reasoning is concerned with simulating cognitive human ability to make presumptions about the type and essence of ordinary situations encountered every day. The most popular way to represent common-sense knowledge is in the form of a semantic graph. Such type of knowledge, however, is known to be rather extensive: the more concepts added in the graph, the harder and slower it becomes to apply standard graph-mining techniques. In this work, we propose a new fast subgraph matching approach to overcome these issues. Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known to be a non-deterministic polynomial time (NP)-complete problem. Many algorithms have been previously proposed to solve this problem using Central Processing Units (CPUs). In this paper, we present a new Graphics Processing Unit (GPU)-friendly method for common-sense subgraph matching, termed GpSense, which is designed for scalable massively-parallel architectures, to enable next-generation Big Data sentiment analysis and Natural Language Processing (NLP) applications. We show that GpSense outperforms state-of-the-art algorithms and efficiently answers subgraph queries on large common-sense graphs.

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Section: Subgraph Matching Algorithmsmentioning

confidence: 99%

Section: Gpu-based Subgraph Matchingmentioning

confidence: 99%

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching

2016

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“…Sun et al [17] study the problem of sub-graph matching on big data graphs. They present an algorithm that supports efficient sub-graph matching for graphs deployed on a distributed memory store.…”

Section: Recent Workmentioning

confidence: 99%

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

Nettleton¹,

Dries²

2015

IJCA

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Approximate sub-graph matching is important in many graph data mining fields. At present, current solutions can be difficult to implement, have an expensive pre-processing phase, or only work for given types of graph. In this paper a novel generic approach is presented which addresses these issues. An approximate sub-graph matcher (A-SGM) calculates the distance between the topological characteristics (footprint) of the sub-graphs to be matched, applying a weighting to the different sub-graph characteristics and those of neighbor nodes. The weights are calibrated for each dataset with a simulated annealing process using sample sets of graph nodes to reduce computational cost, and an exact isomorphism matcher as a fitness function which takes into account how well the match maintains the neighboring node degree distributions. Benchmarking is performed on several state of the art methods and real and synthetic graph datasets to evaluate the precision, recall and computational cost. The results show that the A-SGM is competitive with state of the art methods in terms of precision, recall and execution time.

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“…Subgraph matching has been studied in the graph query processing literature with respect to approximate matches [19], [25], [26], [30] and exact matches [18], [27], [31]. Subgraph match queries have also been proposed for RDF graphs [15], probabilistic graphs [24] and temporal graphs [1].…”

Section: Comparison With Previous Workmentioning

confidence: 99%

Top-K interesting subgraph discovery in information networks

Gupta

Gao

Yan

et al. 2014

2014 IEEE 30th International Conference on Data Engineering

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Abstract-In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. Many problems on such networks can be mapped to an underlying critical problem of discovering top-K subgraphs of entities with rare and surprising associations. Answering such subgraph queries efficiently involves two main challenges: (1) computing all matching subgraphs which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the subgraphs. Previous work on the matching problem can be harnessed for a naïve ranking-after-matching solution. However, for large graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only the top-K matches are desired. In this paper, we address the two challenges of matching and ranking in top-K subgraph discovery as follows. First, we introduce two index structures for the network: topology index, and graph maximum metapath weight index, which are both computed offline. Second, we propose novel top-K mechanisms to exploit these indexes for answering interesting subgraph queries online efficiently. Experimental results on several synthetic datasets and the DBLP and Wikipedia datasets containing thousands of entities show the efficiency and the effectiveness of the proposed approach in computing interesting subgraphs.

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Efficient subgraph matching on billion node graphs

Cited by 296 publications

References 28 publications

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

Top-K interesting subgraph discovery in information networks

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