TY - JOUR
T1 - A joint optimization framework for better community detection based on link prediction in social networks
AU - Zhang, Shu Kai
AU - Li, Cheng Te
AU - Lin, Shou De
N1 - Funding Information:
This work is supported by Taiwan Ministry of Science and Technology (MOST) under Grants 109-2636-E-006-017 (MOST Young Scholar Fellowship), 109-2634-F-002-033 and 108-2218-E-006-036, Academia Sinica under Grant AS-TP-107-M05, Microsoft Research Asia Collaborative Project Funding (2019), and National Center for High-Performance Computing in Taiwan.
Funding Information:
This work is supported by Taiwan Ministry of Science and Technology (MOST) under Grants 109-2636-E-006-017 (MOST Young Scholar Fellowship), 109-2634-F-002-033 and 108-2218-E-006-036, Academia Sinica under Grant AS-TP-107-M05, Microsoft Research Asia Collaborative Project Funding (2019), and National Center for High-Performance Computing in Taiwan.
Publisher Copyright:
© 2020, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities, in this paper, a joint optimization framework, Communities detected on Predicted Edges, is proposed. Our goal to improve the quality of community detection through learning the probability of unseen links and the probability of community affiliation of nodes simultaneously. Link prediction and community detection are mutually reinforced to generate better results of both tasks. Experiments conducted on a number of well-known network data show that the proposed COPE stably outperforms several state-of-the-art community detection algorithms.
AB - Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities, in this paper, a joint optimization framework, Communities detected on Predicted Edges, is proposed. Our goal to improve the quality of community detection through learning the probability of unseen links and the probability of community affiliation of nodes simultaneously. Link prediction and community detection are mutually reinforced to generate better results of both tasks. Experiments conducted on a number of well-known network data show that the proposed COPE stably outperforms several state-of-the-art community detection algorithms.
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U2 - 10.1007/s10115-020-01490-z
DO - 10.1007/s10115-020-01490-z
M3 - Article
AN - SCOPUS:85088149658
SN - 0219-1377
VL - 62
SP - 4277
EP - 4296
JO - Knowledge and Information Systems
JF - Knowledge and Information Systems
IS - 11
ER -