Fast Subgraph Matching on Large Graphs using Graphics Processors

Tran, Ha Nguyen; Kim, Jung‐Jae; He, Bingsheng

doi:10.1007/978-3-319-18120-2_18

Cited by 52 publications

(28 citation statements)

References 29 publications

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“…While some works have targeted at the sequential setting [2], [7], [8], [9], [10], others have focused on parallel solutions [11], [12], [13], [14], [15], [16], [17]. More recently, there have been interests [18], [19] in utilizing graphics processing units (GPU) to accelerate subgraph enumeration. These GPU-based solutions [18], [19] iteratively extend partial instances to match one vertex of P at a time until the instances are found.…”

Section: The Case Of Reusementioning

confidence: 99%

“…More recently, there have been interests [18], [19] in utilizing graphics processing units (GPU) to accelerate subgraph enumeration. These GPU-based solutions [18], [19] iteratively extend partial instances to match one vertex of P at a time until the instances are found. In each iteration, given the partial instances enumerated, we determine a set of candidate vertices of G that match a vertex of P , which is called the candidate set.…”

Section: The Case Of Reusementioning

confidence: 99%

“…The state-of-the-art GPU solutions (NEMO [18] and GPSM [19]) are based on the breadth-first search (BFS) approach as shown in Algorithm 1. It initializes the partial instances with all data vertices.…”

Section: Bfs Approachmentioning

confidence: 99%

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Exploiting Reuse for GPU Subgraph Enumeration

Guo

Tan

2022

IEEE Trans. Knowl. Data Eng.

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Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to 95% of the total processing time. (Un)surprisingly, a significant portion (as high as 99%) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to 5 times speedups compared with the state-of-the-art GPU solutions.

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Section: The Case Of Reusementioning

confidence: 99%

Section: The Case Of Reusementioning

confidence: 99%

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Exploiting Reuse for GPU Subgraph Enumeration

Guo

Tan

2022

IEEE Trans. Knowl. Data Eng.

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“…Based on the above graph structure, we propose a simple and efficient subgraph matching algorithm. The approach is based on a filtering-and-joining strategy which is specially designed for massively parallel computing architectures of modern GPUs [26]. The main routine of the GPU-based method is depicted in Algorithm 1.…”

Section: Gpu-based Subgraph Matchingmentioning

confidence: 99%

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

2016

Self Cite

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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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“…Neighbor queries are the most essential operations , and many other operations such as subgraph query , community finding , graph pattern matching , and outlier detection , can be built based on the neighbor queries. Ranked neighbor queries search for all neighbors of a query token, and the search results are sent back in a ranked sequence based on the ranked relevance criteria (Section 3.2).…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving ranked neighbor query over encrypted graph data in the cloud

Zhu

Xie

2016

Security Comm. Networks

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As cloud computing becomes prevalent, many data owners outsource their large number of graph data into the cloud for cost savings. The cloud may not be fully trusted and usually likes to learn owners' data. To protect the security of graph data, sensitive data have to be stored in an encrypted form. Neighbor queries are the most essential operations, many other operations can be built based on the neighbor queries. Ranked neighbor queries can make the queries very flexible, a query user can get ranked search results. When the graph data are encrypted, the operations over graph data are very inconvenient. Ranked neighbor queries over encrypted graph data are difficult to directly achieve. In this paper, we propose a solution to perform privacy‐preserving ranked neighbor query over encrypted graph data in the cloud, which is named as PRNQ. Our work utilizes ranking function and encryption mechanism to perform ranked neighbor query. The query users can obtain the most wanted search results by this solution. Our paper proposes a design scheme for the weighted directed graph and proves the security and correctness. Experimental results with a real graph data set demonstrate the validity and efficiency of our scheme. Copyright © 2016 John Wiley & Sons, Ltd.

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Fast Subgraph Matching on Large Graphs using Graphics Processors

Cited by 52 publications

References 29 publications

Exploiting Reuse for GPU Subgraph Enumeration

Exploiting Reuse for GPU Subgraph Enumeration

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

Privacy-preserving ranked neighbor query over encrypted graph data in the cloud

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