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    • Efficient Multi-network Community Search Method for Distributed Graph Iterative Computation

    Xiaochen Sun,Wei Wang,Tao Huang

    2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024（2024）

    Chinese Acad Sci

    Cited 0|Views4
    Abstract
    Graph is often used for data analysis. Distributed graph processing is gaining traction as it becomes more difficult for a single machine to store and process the complete graph due to the growing volume of data. We investigated 26 popular distributed graph processing systems and the graph algorithms and datasets provided by these systems. The computational logic of these graph algorithms does not distinguish between the types of vertices and edges, so distributed graph processing systems treat all vertices and edges in an undifferentiated way. However, using the hidden data connections of different types of vertices in multi-networks can greatly improve the accuracy of the community search algorithm. So we describe the challenges for the existing distributed graph processing systems to deal with different types of vertices and edges in multi-networks, and propose an index-based multi-network storage abstraction to store various vertices and edges, and a heuristic greedy algorithm to complete the partition job for different vertices and edges. Base on the two jobs, we finish the research of efficient community search for distributed graph processing in multi-networks, making it possible for future research of more algorithms for distributed graph processing in multi-networks.
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    Key words
    distributed graph processing,community search,graph iterative computation
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