Using Node Identifiers and Community Prior for Graph-Based Classification

Using Node Identifiers and Community Prior for Graph-Based Classification

Blog Article

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 Probiotic 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 Dustbags 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.