Name
Abstract | ||
|---|---|---|
In order to capture the directed relationship between nodes more accurately, this paper proposed a novel network embedding model called BDNE. The model adds the bi-directional distance while preserving the co-occurrence frequency of the nodes within a context window, which is of great significance in some applications of social networks, such as public opinion monitoring and control, and group discovery. Experimental results show that the embedding results of our model as features in the node classification task are better than DeepWalk, Node2Vector and Line on real data sets of different types and sizes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CyberC.2019.00036 | 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) |
Keywords | Field | DocType |
network embedding, representation learning, data mining | Data mining,Data set,Embedding,Social network,Computer science,Group discovery,Computer network,Public opinion,Network embedding,Feature learning,Context window | Conference |
ISSN | ISBN | Citations |
2475-7020 | 978-1-7281-2543-5 | 1 |
PageRank | References | Authors |
0.35 | 2 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongjie Zhu | 1 | 4 | 4.77 |
| Yundong Sun | 2 | 4 | 1.72 |
| Ning Cao | 3 | 1 | 0.35 |
| Xueming Qiao | 4 | 1 | 0.35 |
| Ming Xu | 5 | 1 | 0.35 |
| Li Jin-Lin | 6 | 30 | 4.80 |
| Junzhou Yang | 7 | 1 | 0.35 |