Learning Features of Network Structures Using Graphlets

Abstract

Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic applications, these networks may vary over time. Modeling and analyzing such temporal properties is of additional interest as it can provide a richer characterization of relations between nodes in networks. In this paper, we explore the role of \emph{graphlets} in network classification for both static and temporal networks. Graphlets are small non-isomorphic induced subgraphs representing connected patterns in a network and their frequency can be used to assess network structures. We show that graphlet features, which are not captured by state-of-the-art methods, play a significant role in enhancing the performance of network classification. To that end, we propose two novel graphlet-based techniques, \emph{gl2vec} for network embedding, and \emph{gl-DCNN} for diffusion-convolutional neural networks. We demonstrate the efficacy and usability of \emph{gl2vec} and \emph{gl-DCNN} through extensive experiments using several real-world static and temporal networks. We find that features learned from graphlets can bring notable performance increases to state-of-the-art methods in network analysis.

arXiv commentA conference version has been published in IEEE/ACM ASONAM'19 with title "gl2vec: Learning feature representation using graphlets for directed networks" and can be found at https://dl.acm.org/doi/abs/10.1145/3341161.3342908

References in corpus (13)

• Statistical mechanics of complex networks
• Finding and evaluating community structure in networks
• Modularity and community structure in networks
• Semi-Supervised Classification with Graph Convolutional Networks
• Random graphs with arbitrary degree distributions and their applications
• Classes of behavior of small-world networks
• Loopy Belief Propagation for Approximate Inference: An Empirical Study
• struc2vec: Learning Node Representations from Structural Identity
• graph2vec: Learning Distributed Representations of Graphs
• Discriminative Embeddings of Latent Variable Models for Structured Data
• Temporal motifs in time-dependent networks
• Adversarially Regularized Graph Autoencoder for Graph Embedding
• Network Classification in Temporal Networks Using Motifs