Attributed Network Embedding via a Siamese Neural Network
| Autor: | Jia Peng, Jingjie Mo, Neng Gao, Jiong Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050101 languages & linguistics
Artificial neural network Computer science Semantic proximity business.industry Node (networking) 05 social sciences Network embedding Network structure 02 engineering and technology Similarity (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Hidden layer Artificial intelligence Representation (mathematics) business |
| Zdroj: | SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI |
| Popis: | Recently, network embedding has attracted a surge of attention due to its ability to automatically extract features from graph-structured data. Though network embedding method has been intensively studied, most of the existing approaches pay attention to either structures or attributes. In this paper, we propose a novel attributed network embedding method based on a Siamese neural network, named SANE, to capture both the network structure and node attribute information in a principled way. Specifically, to preserve local semantic proximity, we adopt a Siamese neural network, which can directly learn the similarity of paired nodes with their attributes as input. Then, a skip-gram module is connected with the final shared hidden layer to capture high-order proximity based on the latent representation of node attributes. Thus, we can learn the complex interrelations between nodes. Empirically, we evaluate our model on several real-world datasets and the experimental results have verified the effectiveness of our proposed approach. |
| Databáze: | OpenAIRE |
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