Hierarchical Learning for Large Multi-class Network Classification

Multi-class learning from network data is an important but challenging problem with many applications, including malware detection in computer networks, user modeling in social networks, and protein function prediction in biological networks. Despite the extensive research on large multi-class learning, there are still numerous issues that have not been sufficiently addressed, such as efficiency of model testing, interpretability of the induced models, as well as the ability to handle imbalanced classes. To overcome these challenges, there has been increasing interest in recent years to develop hierarchical learning methods for large multi-class problems. Unfortunately, none of them were designed for classification of network data. In addition, there are very few studies devoted to classification of heterogeneous networks, where the nodes may have different feature sets. In this paper, we propose a hierarchical tree learning approach for large scale network classification task, our method can seamlessly integrate both the link structure and node attribute information into a unified learning framework. To the best of our knowledge, this is the first study that automatically constructs a taxonomy structure to predict large multi-class problems for network classification. Empirical results suggest that the approach can effectively capture the relationship between classes and generate class taxonomy structures that resemble those produced by human experts. The approach can also be easily parallelizable and has been implemented in a MapReduce framework.