Link Prediction in Dynamic Networks Using Graphlet

Predicting the link state of a network at a future time given a collection of link states at earlier time is an important task with many real-life applications. In existing literature this task is known as link prediction in dynamic networks. Solving this task is more difficult than its counterpart in static networks because an effective feature representation of node-pair instances for the case of dynamic network is hard to obtain. In this work we solve this problem by designing a novel graphlet transition based feature representation of the node-pair instances of a dynamic network. We propose a method GraTFEL which uses unsupervised feature learning methodologies on graphlet transition based features to give a low-dimensional feature representation of the node-pair instances. GraTFEL models the feature learning task as an optimal coding task where the objective is to minimize the reconstruction error, and it solves this optimization task by using a gradient descent method. We validate the effectiveness of the learned feature representations by utilizing it for link prediction in real-life dynamic networks. Specifically, we show that GraTFEL, which use the extracted feature representation of graphlet transition events, outperforms existing methods that use well-known link prediction features. The data and software related to this paper are available at https://github.com/DMGroup-IUPUI/GraTFEL-Source.