Finding and Counting Tree-Like Subgraphs Using MapReduce

Zhao, Zhao; Chen, Langshi; Avram, Mihai; Li, Meng; Wang, Guanying; Butt, Ali R.; Khan, Maleq; Marathe, Madhav V.; Qiu, Judy; Vullikanti, Anil

doi:10.1109/tmscs.2017.2768426

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…A tree subgraph enumeration algorithm by combining color coding with a stream-based cover decomposition was developed in [17]. To process massive networks, [18] developed a distributed color-coding based tree counting solution upon the MapReduce framework in Hadoop, [19] implemented an MPIbased solution, and [20] [21] pushed the limit of subgraph counting to process billion-edged networks and trees up to 15 vertices. [22] developed a coloring method that achieves a provable confidence value in a small number of iterations.…”

Section: Related Workmentioning

confidence: 99%

SubGraph2Vec: Highly-Vectorized Tree-like Subgraph Counting

Chen

Sahinalp

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Subgraph counting aims to count occurrences of a template T in a given network G(V, E). It is a powerful graph analysis tool and has found real-world applications in diverse domains. Scaling subgraph counting problems is known to be memory bounded and computationally challenging with exponential complexity. Although scalable parallel algorithms are known for several graph problems such as Triangle Counting and PageRank, this is not common for counting complex subgraphs. Here we address this challenge and study connected acyclic graphs or trees. We propose a novel vectorized subgraph counting algorithm, named SUBGRAPH2VEC, as well as both shared memory and distributed implementations: 1) reducing algorithmic complexity by minimizing neighbor traversal; 2) achieving a highly-vectorized implementation upon linear algebra kernels to significantly improve performance and hardware utilization. 3) SUBGRAPH2VEC improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x on a single node. 4) SUBGRAPH2VEC in distributed mode can scale up the template size to 20 and maintain good strong scalability. 5) enabling portability to both CPU and GPU.

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Section: Related Workmentioning

confidence: 99%

SubGraph2Vec: Highly-Vectorized Tree-like Subgraph Counting

Chen

Sahinalp

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

Self Cite

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“…A tree subgraph enumeration algorithm by combining color coding with a stream-based cover decomposition was developed in [40]. To process massive networks, [41] developed a distributed version of color-coding based tree counting solution upon MapReduce framework in Hadoop, [33] implemented a MPI-based solution, and [39] [15] pushed the limit of subgraph counting to process billion-edged networks and trees up to 15 vertices.…”

Section: Background and Related Work 21 Subgraph Counting By Color Co...mentioning

confidence: 99%

A GraphBLAS Approach for Subgraph Counting

Chen,

Li,

Azad

et al. 2019

Preprint

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Subgraph counting aims to count the occurrences of a subgraph template T in a given network G. The basic problem of computing structural properties such as counting triangles and other subgraphs has found applications in diverse domains. Recent biological, social, cybersecurity and sensor network applications have motivated solving such problems on massive networks with billions of vertices. The larger subgraph problem is known to be memory bounded and computationally challenging to scale; the complexity grows both as a function of T and G. In this paper, we study the non-induced tree subgraph counting problem, propose a novel layered softwarehardware co-design approach, and implement a shared-memory multi-threaded algorithm: 1) reducing the complexity of the parallel color-coding algorithm by identifying and pruning redundant graph traversal; 2) achieving a fully-vectorized implementation upon linear algebra kernels inspired by GraphBLAS, which significantly improves cache usage and maximizes memory bandwidth utilization. Experiments show that our implementation improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x for subgraph templates with size over 12 on a dual-socket Intel(R) Xeon(R) Platinum 8160 server. We believe our approach using GraphBLAS with optimized sparse linear algebra can be applied to other massive subgraph counting problems and emerging high-memory bandwidth hardware architectures.

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Finding and Counting Tree-Like Subgraphs Using MapReduce

Cited by 2 publications

References 40 publications

SubGraph2Vec: Highly-Vectorized Tree-like Subgraph Counting

SubGraph2Vec: Highly-Vectorized Tree-like Subgraph Counting

A GraphBLAS Approach for Subgraph Counting

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