Name
Abstract | ||
|---|---|---|
•Our PSQP identifies both top-k core members and their most important relations.•The effectiveness of PSQP is well explained in theory and verified by experiments.•We have fully discussed the different types of usages for PSQP in practice. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.eswa.2014.04.001 | Expert Systems with Applications |
Keywords | Field | DocType |
Poly-relational networks,Top-k core members,Importance weight,Sequential quadratic programming | Importance Weight,Data mining,Social network,Computer science,Artificial intelligence,Sequential quadratic programming,Partition (number theory),Machine learning | Journal |
Volume | Issue | ISSN |
41 | 13 | 0957-4174 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Huang | 1 | 589 | 104.49 |
| Yunjun Gao | 2 | 862 | 89.71 |
| Kevin Chiew | 3 | 116 | 11.06 |
| Qinming He | 4 | 371 | 41.53 |
| Baihua Zheng | 5 | 1850 | 101.64 |