Decoupled Variational Embedding for Signed Directed Networks
- URL: http://arxiv.org/abs/2008.12450v1
- Date: Fri, 28 Aug 2020 02:48:15 GMT
- Title: Decoupled Variational Embedding for Signed Directed Networks
- Authors: Xu Chen and Jiangchao Yao and Maosen Li and Ya zhang and Yanfeng Wang
- Abstract summary: We propose to learn more representative node embeddings by simultaneously capturing the first-order and high-order topology in signed directed networks.
In particular, we reformulate the representation learning problem on signed directed networks from a variational auto-encoding perspective.
Extensive experiments are conducted on three widely used real-world datasets.
- Score: 39.3449157396596
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Node representation learning for signed directed networks has received
considerable attention in many real-world applications such as link sign
prediction, node classification and node recommendation. The challenge lies in
how to adequately encode the complex topological information of the networks.
Recent studies mainly focus on preserving the first-order network topology
which indicates the closeness relationships of nodes. However, these methods
generally fail to capture the high-order topology which indicates the local
structures of nodes and serves as an essential characteristic of the network
topology. In addition, for the first-order topology, the additional value of
non-existent links is largely ignored. In this paper, we propose to learn more
representative node embeddings by simultaneously capturing the first-order and
high-order topology in signed directed networks. In particular, we reformulate
the representation learning problem on signed directed networks from a
variational auto-encoding perspective and further develop a decoupled
variational embedding (DVE) method. DVE leverages a specially designed
auto-encoder structure to capture both the first-order and high-order topology
of signed directed networks, and thus learns more representative node
embedding. Extensive experiments are conducted on three widely used real-world
datasets. Comprehensive results on both link sign prediction and node
recommendation task demonstrate the effectiveness of DVE. Qualitative results
and analysis are also given to provide a better understanding of DVE.
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