Combining Node Identifier Features And Community Priors For Within-Network Classification

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 paper, 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. On average, our approach can provide Hamming score, Micro-F-1 score and Macro-F-1 score up to 14%, 21% and 14% higher than competing methods respectively in sparsely labeled networks. The experiment results show that our approach is quite efficient and suitable for large-scale real-world classification tasks.