| Abstract |
Learning network representations has a variety of applications, such as network classification. Most existing work in this area focuses on static undirected networks and do not account for pres- ence of directed edges or temporarily changes. Furthermore, most work focuses on node representations that do poorly on tasks like network classification. In this paper, we propose a novel, flexible and scalable network embedding methodology, gl2vec, for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or tempo- ral network graphlet distributions and a null model for comparing them against random graphs. We argue that gl2vec can be used to classify and compare networks of varying sizes and time period with high accuracy. We demonstrate the efficacy and usability of gl2vec over existing state-of-the-art methods on network classi- fication tasks such as network type classification and subgraph identification in several real-world static and temporal directed net- works. Experimental results further show that gl2vec, concatenated with a wide range of state-of-the-art methods, improves classifi- cation accuracy by up to 10% in real-world applications such as detecting departments for subgraphs in an email network or identi- fying mobile users given their app switching behaviors represented as static or temporal directed networks. |