Name
Abstract | ||
|---|---|---|
Disappearing link prediction aims to predict the possibility of the links disappearing in the future. This paper describes the disappearing link prediction problem in scientific collaboration networks based on network embedding. We propose a novel network embedding method called TDL2vec, which is an extension of node2vec algorithm. TDL2vec generates the link embdeddings considering with the time factor. In this paper, the disappearing link prediction problem is considered as a binary classification problem, and support vector machine (SVM) is used as the classifier after link embedding. To evaluate the performance in disappearing link prediction, this paper tests the proposed method and several baseline methods on a real-world network. The experimental results show that TDL2vec achieves better performance than other baselines. |
Year | Venue | Field |
|---|---|---|
2017 | DASC/PiCom/DataCom/CyberSciTech | Data mining,Embedding,Task analysis,Binary classification,Computer science,Support vector machine,Time factor,Prediction algorithms,Network embedding,Classifier (linguistics) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Li | 1 | 47 | 16.04 |
| Wei Wang | 2 | 7122 | 746.33 |
| Shuo Yu | 3 | 68 | 13.95 |
| Liangtian Wan | 4 | 116 | 11.60 |
| Zhenzhen Xu | 5 | 80 | 11.66 |
| Xiangjie Kong | 6 | 425 | 46.56 |