Name
Title | ||
|---|---|---|
The Deep Fusion of Topological Structure and Attribute Information for Link Prediction |
Abstract | ||
|---|---|---|
The link prediction can be used to seek missing or future links in the network, so it has become a hot research topic. The network generally contains two types of information: the topological structure of network formed by the connection between nodes, and the attribute information of nodes. However, the existing topology-based link prediction algorithms consider little attribute information. In this paper, a novel algorithm called Network Embedding with Attribute Deep Fusion for Link Prediction (NEADF-LP) is proposed. We get the embedded vectors with topological structure and attribute information by structure encoder and attribute encoder respectively, and fuse two vectors deeply. Compared with mainstream baselines on CiteSeer and Cora datasets, the results show that the deep fusion of topological structure and attribute information improve the accuracy of link prediction effectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2974016 | IEEE ACCESS |
Keywords | DocType | Volume |
Prediction algorithms,Measurement,Predictive models,Indexes,Social network services,Fuses,Complex network,deep fusion,link prediction,network embedding | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingqiang Zhou | 1 | 0 | 1.35 |
| Yihan Kong | 2 | 0 | 0.34 |
| Shenshen Zhang | 3 | 0 | 0.34 |
| Dan Liu | 4 | 25 | 8.89 |
| Haijiang Jin | 5 | 0 | 0.34 |