|
|
|
A biased edge enhancement method for truss-based community search |
Yuqi LI1, Tao MENG1( ), Zhixiong HE2, Haiyan LIU3, Keqin LI4 |
1. School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410082, China
2. School of Business, Central South University of Forestry and Technology, Changsha 410082, China
3. College of Information Engineering, Changsha Medical University, Changsha 410219, China
4. Department of Computer Science, State University of New York, New York 12561, USA |
|
|
|
|
|
|
Corresponding Author(s):
Tao MENG
|
|
Just Accepted Date: 17 January 2024
Issue Date: 14 March 2024
|
|
| 1 |
E, Akbas P Zhao . Truss-based community search: a truss-equivalence based indexing approach. Proceedings of the VLDB Endowment, 2017, 10( 11): 1298–1309
|
| 2 |
Huang X, Cheng H, Qin L, Tian W, Yu J X. Querying k-truss community in large and dynamic graphs. In: Proceedings of 2014 ACM SIGMOD International Conference on Management of Data. 2014, 1311–1322
|
| 3 |
Y, Fang Z, Wang R, Cheng H, Wang J Hu . Effective and efficient community search over large directed graphs. IEEE Transactions on Knowledge and Data Engineering, 2019, 31( 11): 2093–2107
|
| 4 |
Cui W, Xiao Y, Wang H, Wang W. Local search of communities in large graphs. In: Proceedings of 2014 ACM SIGMOD International Conference on Management of Data. 2014, 991–1002
|
| 5 |
W, Luo D, Zhang H, Jiang L, Ni Y Hu . Local community detection with the dynamic membership function. IEEE Transactions on Fuzzy Systems, 2018, 26( 5): 3136–3150
|
| 6 |
W, Luo N, Lu L, Ni W, Zhu W Ding . Local community detection by the nearest nodes with greater centrality. Information Sciences, 2020, 517: 377–392
|
| 7 |
T, Xu Z, Lu Y Zhu . Efficient triangle-connected truss community search in dynamic graphs. Proceedings of the VLDB Endowment, 2022, 16( 3): 519–531
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
