Name
Abstract | ||
|---|---|---|
Network embedding aims to learn the low-dimensional representations of nodes in networks and preserve the features of networks simultaneously. In this paper, we propose a novel approach of network embedding, named Attributed Network Embedding with Data Distribution Adaptation (ANEDDA). In our model, we consider network structure and node attributes as two kinds of data which have different probability distributions. This is because that they come from different aspects or views and capture different kinds of features. However, because they come from the same network, there must exist some latent features which are related to both two kinds of information, and these features are significant to detect the community structure and analyze the network. We utilize a domain adaptation model named Transfer Component Analysis (TCA) to identify the latent common features. The proposed method is designed for unweighted networks (graphs), and it integrates the structure information and nodes attributes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BESC.2018.8697328 | 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC) |
Keywords | Field | DocType |
network embedding,node attributes,topological structure information,data distribution adaptation | Graph,Community structure,Domain adaptation,Computer science,Theoretical computer science,Probability distribution,Network embedding,Component analysis,Network structure | Conference |
ISBN | Citations | PageRank |
978-1-7281-0207-8 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaxing Chen | 1 | 0 | 1.35 |
| Yanghui Rao | 2 | 31 | 3.57 |
| Runxuan Chen | 3 | 0 | 0.68 |
| Qi Dai | 4 | 33 | 6.85 |
| Xiaofeng Li | 5 | 0 | 0.68 |
| Ruixin Wang | 6 | 0 | 0.34 |