TY - GEN
T1 - Parameter-Free Structural Diversity Search
AU - Huang, Jinbin
AU - HUANG, Xin
AU - Zhu, Yuanyuan
AU - XU, Jianliang
N1 - Funding Information:
This work is supported by the NSFC Nos. 61702435, 61972291, RGC Nos. 12200917, 12200817, CRF C6030-18GF, and the National Science Foundation of Hubei Province No. 2018CFB519.
PY - 2019/10/29
Y1 - 2019/10/29
N2 - The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of which automatically models various kinds of social contexts without parameter t. Leveraging on and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.
AB - The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of which automatically models various kinds of social contexts without parameter t. Leveraging on and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.
UR - http://www.scopus.com/inward/record.url?scp=85077007022&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34223-4_43
DO - 10.1007/978-3-030-34223-4_43
M3 - Conference contribution
AN - SCOPUS:85077007022
SN - 9783030342227
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 677
EP - 693
BT - Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings
A2 - Cheng, Reynold
A2 - Mamoulis, Nikos
A2 - Sun, Yizhou
A2 - Huang, Xin
PB - Springer
T2 - 20th International Conference on Web Information Systems Engineering, WISE 2019
Y2 - 26 November 2019 through 30 November 2019
ER -