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Title: Distributed Top-k Subgraph Matching in A Big Graph
Subgraph matching query is to find out the subgraphs of data graph G which match a given query graph Q. Traditional methods can not deal with big data graphs due to their high computational complex. In this paper, we propose a distributed top-k subgraph search method over big graphs. The proposed method is designed at the level of single vertex and all vertices obtain their matching state separately without requiring global graph information. Therefore, it can be easily deployed in distributed platform like Hadoop. The evaluations of running time, number of messages and supersteps show the efficiency and scalability of the proposed method.
Authors:
Award ID(s):
1815256
Publication Date:
NSF-PAR ID:
10111470
Journal Name:
IEEE International Conference on Big Data
Page Range or eLocation-ID:
5325-5335
ISSN:
2639-1589
Sponsoring Org:
National Science Foundation
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