Name
Abstract | ||
|---|---|---|
A reasonable and effective community scoring function is of great significance since it can measure the community quality of groups we found more properly and help us discover more valuable communities. In this paper, we propose a new community scoring function, ECOQUG. Different from the existing community scoring functions, ECOQUG is designed based on the experimental study and theoretical analysis of groups with different community qualities. ECOQUG is more convincing. In addition, we design a series of experiments to examine the effectiveness and accuracy of ECOQUG and 13 other classic community scoring functions comprehensively. The extensive experimental results show that ECOQUG is effective and better than other community scoring functions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDE.2019.00177 | 2019 IEEE 35th International Conference on Data Engineering (ICDE) |
Keywords | Field | DocType |
YouTube,Perturbation methods,Measurement,Conferences,Data engineering,Computer science,Search problems | Data mining,Computer science,Information engineering,Artificial intelligence,Machine learning | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-5386-7474-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunnan Wang | 1 | 1 | 2.03 |
| Hongzhi Wang | 2 | 421 | 73.72 |
| Chang Zhou | 3 | 184 | 21.75 |
| Jianzhong Li | 4 | 63 | 24.23 |
| Hong Gao | 5 | 1086 | 120.07 |