Name
Abstract | ||
|---|---|---|
Network representation is the basis of many applications and of extensive interest in various fields such as information retrieval, social network analysis, and recommendation systems. Majority of previous methods on network representation only considered incomplete aspects of the problem, such as link structure, node information, or partial integration. The present paper proposes a comprehensive network representation model, which seamlessly integrates the text information, node label, and first-order and second-order proximity of a network. The effectiveness of the introduced strategies is experimentally evaluated. Results demonstrate that our method is better than state-of-the-art techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3041021.3054241 | WWW (Companion Volume) |
Field | DocType | Citations |
Recommender system,Dynamic network analysis,Organizational network analysis,Data mining,Information networks,Computer science,Social network analysis,Network simulation,Artificial intelligence,Feature learning,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hang Li | 1 | 38 | 21.94 |
| Haozheng Wang | 2 | 9 | 2.19 |
| Zhenglu Yang | 3 | 257 | 35.45 |
| Jinmao Wei | 4 | 23 | 6.46 |
| Masato Odagaki | 5 | 7 | 1.49 |