Domain-Invariant Latent Representation Discovers Roles

Kikuta, Shumpei; Toriumi, Fujio; Nishiguchi, Mao; Fukuma, Tomoki; Nishida, Takanori; Usui, Shohei

doi:10.1007/978-3-030-36687-2_69

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2020

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“…This paper extends a previous study (Kikuta et al 2019) by proposing a general framework, presenting a new model selection method, and conducting experiments on different datasets.…”

Section: Introductionmentioning

confidence: 75%

Framework for role discovery using transfer learning

et al. 2020

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Discovering the node roles in a network helps to solve diverse social problems. Role discovery attempts to predict the node roles from a network structure, and this method has been extensively studied in various fields. Role discovery using transfer learning has many advantages, but methods using this approach face two kinds of problems: domain-shift problems and model selection. To address these problems, we propose a general framework that includes network representation learning, domain adversarial learning for suppressing domain-shift problems, and model selection without using target labels. As a result of computational experiments, we show on publicly available datasets that the proposed model outperforms conventional methods, the proposed model selection method performs well without using target labels, and the proposed method can be used in real-world datasets. Furthermore, we found that our framework suppressed domain-shift problems, worked well even with differences between networks, and could handle imbalanced classes.

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“…This paper extends a previous study (Kikuta et al 2019) by proposing a general framework, presenting a new model selection method, and conducting experiments on different datasets.…”

Section: Introductionmentioning

confidence: 75%