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A GraphBLAS Approach for Subgraph Counting
Subgraph counting aims to count the occurrences of a subgraph template T...
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High-Performance Massive Subgraph Counting using Pipelined Adaptive-Group Communication
Subgraph counting aims to count the number of occurrences of a subgraph ...
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GSI: GPU-friendly Subgraph Isomorphism
Subgraph isomorphism is a well-known NP-hard problem that is widely used...
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Experimental Evaluation of Counting Subgraph Isomorphisms in Classes of Bounded Expansion
Counting subgraph isomorphisms (also called motifs or graphlets) has bee...
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A decentralized algorithm for network node counting
Node counting on a graph is subject to some fundamental theoretical limi...
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A Survey on Subgraph Counting: Concepts, Algorithms and Applications to Network Motifs and Graphlets
Computing subgraph frequencies is a fundamental task that lies at the co...
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Fast and Robust Distributed Subgraph Enumeration
We study the classic subgraph enumeration problem under distributed sett...
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SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting
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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