Using Node Identifiers and Community Prior for Graph-Based Classification

Abstract

With widely available large-scale network data, one hot topic is how to adopt traditional classification algorithms to predict the most probable labels of nodes in a partially labeled network. In this article, we propose a new algorithm called identifier-based relational neighbor classifier (IDRN) to solve the within-network multi-label classification problem. We use the node identifiers in the egocentric networks as features and propose a within-network classification model by incorporating community structure information to predict the most probable classes for unlabeled nodes. We demonstrate the effectiveness of our approach on several publicly available datasets. First, taking a semi-supervised approach, IDRN without any community prior is applied in community detection experiments, and it outperforms most existing unsupervised community detection algorithms. After that, in large-scale graph-based multi-label classification tasks, our approaches perform well in both fully labeled and partially labeled networks in most cases. To evaluate the scalability of our algorithm, we also show a scalability test to evaluate the running time of our algorithm in different networks. The experiment results show that our approach is quite efficient and suitable for large-scale real-world classification tasks.