gl2vec: Learning Feature Representation Using Graphlets for Directed Networks

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 presence 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, \emph{gl2vec}, for network classification in both static and temporal directed networks. \emph{gl2vec} constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs. We argue that \emph{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 \emph{gl2vec} over existing state-of-the-art methods on network classification tasks such as network type classification and subgraph identification in several real-world static and temporal directed networks. Experimental results further show that \emph{gl2vec}, concatenated with a wide range of state-of-the-art methods, improves classification accuracy by up to $10\%$ in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks.

Network Classification in Temporal Networks Using Motifs

Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accuracy by up $10\%$ compared to the state-of-the-art embedding method in network classification, for tasks such as classifying network type, identifying communities in email exchange network, and identifying users given their app-switching behaviors.