TY - GEN
T1 - Noise-enhanced community detection
AU - Abdolazimi, Reyhaneh
AU - Jin, Shengmin
AU - Zafarani, Reza
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/13
Y1 - 2020/7/13
N2 - Community structure plays a significant role in uncovering the structure of a network. While many community detection algorithms have been introduced, improving the quality of detected communities is still an open problem. In many areas of science, adding noise improves system performance and algorithm efficiency, motivating us to also explore the possibility of adding noise to improve community detection algorithms. We propose a noise-enhanced community detection framework that improves communities detected by existing community detection methods. The framework introduces three noise methods to help detect communities better. Theoretical justification and extensive experiments on synthetic and real-world datasets show that our framework helps community detection methods find better communities.
AB - Community structure plays a significant role in uncovering the structure of a network. While many community detection algorithms have been introduced, improving the quality of detected communities is still an open problem. In many areas of science, adding noise improves system performance and algorithm efficiency, motivating us to also explore the possibility of adding noise to improve community detection algorithms. We propose a noise-enhanced community detection framework that improves communities detected by existing community detection methods. The framework introduces three noise methods to help detect communities better. Theoretical justification and extensive experiments on synthetic and real-world datasets show that our framework helps community detection methods find better communities.
KW - Community detection
KW - Graph mining
KW - Noise-enhanced methods
UR - http://www.scopus.com/inward/record.url?scp=85089489395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089489395&partnerID=8YFLogxK
U2 - 10.1145/3372923.3404788
DO - 10.1145/3372923.3404788
M3 - Conference contribution
AN - SCOPUS:85089489395
T3 - Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020
SP - 271
EP - 280
BT - Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020
PB - Association for Computing Machinery, Inc
T2 - 31st ACM Conference on Hypertext and Social Media, HT 2020
Y2 - 13 July 2020 through 15 July 2020
ER -