Abstract
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.
Highlights
Each individual node in a network can have different functions, which are called “roles"
In this paper, we proposed a general framework using transfer learning for inferring node roles more accurately
To overcome domain-shift problems, where node representations are slightly different between domains, we tailored domain adversarial learning for role discovery
Summary
Each individual node in a network can have different functions, which are called “roles" (e.g. central nodes, peripheral nodes, clique members). A few studies (Ribeiro et al 2017; Henderson et al 2012) have investigated transfer learning for role discovery, but their methods are likely to encounter domain-shift (Pan et al 2010) problems, where feature representations (i.e. domains) are slightly different between the source and target domains. In such cases, when a model trained on a source domain is applied to a target domain, the model experiences a significant drop in performance. We use domain adversarial learning, which makes the two node representations both similar and discriminative for role discovery Another difficulty in transfer learning is model selection. 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
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