Network representation with clustering tree features

Representing and inferring interaction networks is a challenging and long-standing problem. Modern technological advances have led to a great increase in both volume and complexity of generated network data. The size of networks such as drug protein interaction networks or gene regulatory networks is constantly growing and multiple sources of information are exploited to extract features describing the nodes in such networks. Modern information systems need therefore methods that are able to mine these networks and exploit the available features. Here, a novel data mining framework for network representation and mining is proposed. It is based on decision tree learning and ensembles of trees. The proposed scheme introduces an efficient network data representation, capable of addressing different data types, tackling as well data volume and complexity. The learning process follows the inductive setup and it can be performed in both a supervised or unsupervised manner. Experiments were conducted on six biomedical network datasets. The experimental evaluation demonstrates the merits of the proposed approach, confirming its efficiency.