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Domain-adversarial Network Alignment
[article]
2019
arXiv
pre-print
Network alignment is a critical task to 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
arXiv:1908.05429v1
fatcat:hupxtj4r2rh7pburrj5ymfj6ci