Random Walk Based Resource Allocation: Predicting and Recommending Links in Cross-Operator Mobile Communication Networks

Link prediction is an important issue in social network analysis. It aims at estimating the likelihood of the existence of links between nodes by the known network information. Although this problem has been extensively studied, many significant factors about how to calculate the similarity between two nodes remains unexplored and largely open, especially for the mobile phone networks. We are first to take the link prediction task for missing calls in cross-operator communication networks. This task is of interest from not only a purely scientific point of view but also an excellent scenario for the marketing of telecom operators. We develop several algorithms based on a physical concept: resource allocation that treats all nodes in a network differently by allocating resource to each one. We use the call attributes of each node as resources which are owned by themselves. The process of resource allocation is guided by weighted random walk which naturally combines the information from network structure with call attributes of nodes and edges. Our experiments on the cross-operator networks show that our newly proposed algorithms outperform the other state-of-the-art unsupervised approaches as well as the supervised HPLP method.