Subgraph Matching on Multiple Graph Streams

Van, Duong Thi Thu; Dolgorsuren, Batjargal; Lee, Young-Koo

doi:10.1109/bigcomp54360.2022.00030

Cited by 2 publications

(1 citation statement)

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“…With the advancements in big data and artificial intelligence technology, graphs are widely used to process and analyze various relationships between objects [3,4]. Recently, with the advancements in real-time application services and convergence services, graph streams have been created in which the vertices and edges that comprise the graph change continuously [5,6]. These graph streams are used in fields such as social media analysis, anomaly detection, fraud detection, and content recommendation [7,8].…”

Section: Introductionmentioning

confidence: 99%

Incremental Connected Component Detection for Graph Streams on GPU

et al. 2023

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Studies on the real-time detection of connected components in graph streams have been carried out. The existing connected component detection method cannot process connected components incrementally, and the performance deteriorates due to frequent data transmission when GPU is used. In this paper, we propose a new incremental processing method to solve the problems found in the existing methods for detecting connected components on GPUs. The proposed method minimizes the amount of data to be sent to the GPU by determining the subgraph affected by the graph stream update and by detecting the part to be recalculated. We consider the number of vertices to quickly determine the connected components of a graph stream on the GPU. An asynchronous execution method is used to shorten the transfer time between the CPU and the GPU according to real-time graph stream changes. In order to show that the proposed method provides fast incremental connected component detection on the GPU, we evaluated its performance using various datasets.

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