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Domain-Adversarial Network Alignment
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DOI：10.1109/TKDE.2020.3023589.png)
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摘要
En 中文
Network alignment is a critical task in a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain representation bias induced by domain-dependent features, which yield inferior alignment performance. This paper proposes a unified deep architecture (DANA) to obtain a domain-invariant representation for network alignment via an adversarial domain classifier. Specifically, we employ the graph convolutional networks to perform network embedding under the domain adversarial principle, given a small set of observed anchors. Then, the semi-supervised learning framework is optimized by maximizing a posterior probability distribution of observed anchors and the loss of a domain classifier simultaneously. We also develop a few variants of our model, such as, direction-aware network alignment, weight-sharing for directed networks and simplification of parameter space. Experiments on three real-world social network datasets demonstrate that our proposed approaches achieve state-of-the-art alignment results.
Keyword:
Task analysis
Social network services
Feature extraction
Support vector machines
Probability distribution
Proteins
Two dimensional displays
Network alignment
representation learning
adversarial learning
graph convolutional networks
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