Name
Abstract | ||
|---|---|---|
Network embedding is an effective method to learn a low-dimensional feature vector representation for each node of a given network. In this paper, we propose a deep network embedding model with aggregated proximity preserving (DNE-APP). Firstly, an overall network proximity matrix is generated to capture both local and global network structural information, by aggregating different k-th order network proximities between different nodes. Then, a semi-supervised stacked auto-encoder is employed to learn the hidden representations which can best preserve the aggregated proximity in the original network, and also map the node pairs with higher proximity closer to each other in the embedding space. With the hidden representations learned by DNE-APP, we apply vector-based machine learning techniques to conduct node classification and link label prediction tasks on the real-world datasets. Experimental results demonstrate the superiority of our proposed DNE-APP model over the state-of-the-art network embedding algorithms.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3110025.3110035 | ASONAM '17: Advances in Social Networks Analysis and Mining 2017
Sydney
Australia
July, 2017 |
Keywords | Field | DocType |
Network embedding,graph representation,stacked auto-encoder,semi-supervised,network proximity | Data mining,Feature vector,Embedding,Global network,Computer science,Matrix (mathematics),Effective method,Network embedding,Graph (abstract data type) | Conference |
ISSN | ISBN | Citations |
2473-9928 | 978-1-4503-4993-2 | 3 |
PageRank | References | Authors |
0.38 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Shen | 1 | 42 | 3.33 |
| Fu-lai Chung | 2 | 244 | 34.50 |