Learning to Identify High Betweenness Centrality Nodes from Scratch-Reference-Cited by-同舟云学术

Learning to Identify High Betweenness Centrality Nodes from Scratch

Published:2019-11-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Information and Knowledge Management
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Author:

Fan Changjun¹,Zeng Li²,Ding Yuhui³,Chen Muhao⁴,Sun Yizhou⁵,Liu Zhong²

Affiliation:

1. National University of Defense Technology & University of California, Los Angeles, Changsha, China

2. National University of Defense Technology, Changsha, China

3. Tsinghua University, Beijing, China

4. University of California, Los Angeles & University of Pennsylvania, Los Angeles, CA, USA

5. University of California, Los Angeles, Los Angeles, CA, USA

Funder

NSF CAREER Award

Amazon Research Award

NSF III

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3357384.3357979

Reference37 articles.

1. Ziyad AlGhamdi Fuad Jamour Spiros Skiadopoulos and Panos Kalnis. 2017. A benchmark for betweenness centrality approximation algorithms on large graphs. In SSDBM. 6. Ziyad AlGhamdi Fuad Jamour Spiros Skiadopoulos and Panos Kalnis. 2017. A benchmark for betweenness centrality approximation algorithms on large graphs. In SSDBM. 6.

2. Michele Borassi and Emanuele Natale. 2016. KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation. In ESA . Michele Borassi and Emanuele Natale. 2016. KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation. In ESA .

3. A faster algorithm for betweenness centrality*

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