Functional node detection on linked data

Networks, which characterize object relationships, are ubiquitous in various domains. One very important problem is to detect the nodes of a specific function in these networks. For example, is a user normal or anomalous in an Email network? Does a protein play a key role in a protein-protein interaction network? In many applications, the information we have about the networks usually includes both node characteristics and network structures. Both types of information can contribute to the task of learning functional nodes, and we call the collection of node and link information as linked data. However, existing methods only use a few subjectively selected topological features from network structures to detect functional nodes, thus fail to include highly discriminative and meaningful patterns hidden in linked data. To address this problem, a novel Feature integration based Functional Node Detection (FIND) algorithm is presented. Specifically, FIND extracts the most discriminative information from both node characteristics and network structures in the form of a unified latent feature representation with the guidance of several labeled nodes. Experiments on two real world data sets validate that the proposed method significantly outperforms the baselines on the detection of three different types of functional nodes.