Name
Abstract | ||
|---|---|---|
An approach for link prediction via the local structural information in complex networks is proposed in this paper. In this method, we reference to the thought of Strong Community Structural to achieve link prediction. The approach think that connections among a small community which consists of a pair of predicted nodes and their common neighbors are more important than connections within small community, so we call this approach Small Community (SC) index. Then, in order to promote the efficiency of forecasting, we simplify SC. In this paper, we also consider the different attributes of links which contain directions and weights. Therefore, we change three classical indices (Common Neighbors (CN) index, Resource Allocation (RA) index, Local Path (LP) index) and two simplified SC indices, at the same time, these indices are used in multifarious networks. We evaluate these indices by experiments in appropriate networks. The results show that the two simplified SC indices both achieve great success through comprehensive analysis forecast accuracies and actual computation time. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) | link prediction, local structure information, network structure, similarity, directed weighted network |
Field | DocType | Citations |
Resource management,Data mining,Mathematical optimization,Computer science,Prediction algorithms,Resource allocation,Complex network,Computation | Conference | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Gao | 1 | 24 | 5.20 |
| Lihua Zhou | 2 | 18 | 7.71 |
| Xiaoxuan Wang | 3 | 17 | 7.52 |
| Hongmei Chen | 4 | 25 | 5.39 |