Conference proceeding
Distributed Top-k Subgraph Matching in A Big Graph
2018 IEEE International Conference on Big Data (Big Data), pp 5325-5327
Dec 2018
Abstract
Subgraph matching query is to find out the sub-graphs 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.
Metrics
Details
- Title
- Distributed Top-k Subgraph Matching in A Big Graph
- Creators
- Jianliang Gao - School of Information Science and Engineering, Central South University, Changsha, ChinaChuqi Lei - School of Information Science and Engineering, Central South University, Changsha, ChinaLing Tian - School of Information Science and Engineering, Central South University, Changsha, ChinaYuan Ling - Amazon Alexa AI, USAZheng Chen - College of Information, Drexel University, Philadelphia, USABo Song - Drexel University, Information Science
- Publication Details
- 2018 IEEE International Conference on Big Data (Big Data), pp 5325-5327
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000468499305076
- Scopus ID
- 2-s2.0-85062588536
- Other Identifier
- 991019170515604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods