Problems involving networks and massive network datasets motivate some of the most difficult and exciting inferential challenges in the social and information sciences. Modern network datasets in these areas represent complex relationships with rich information on vertex attributes, edge weights, multiple types of vertices and characteristics, all of which may be changing over time. These datasets are often enormous in size, detail, and heterogeneity, pushing the limits of existing inferential frameworks, while also requiring detailed domain knowledge in order to support useful inferences or predictions. Much progress has been made on developing rigorous tools for analyzing and modeling some types of large real-world social and information network datasets, but often this progress is distributed across disparate applied and theoretical domains. Network analysis is still a young and highly cross-disciplinary field, and the goal of this workshop is to promote cross-pollination between its constituent research communities.

In particular, this workshop aims to bring together a diverse and cross-disciplinary set of researchers to discuss recent advances and future directions for developing new network methods in statistics and machine learning. By network methods, we broadly include those models and algorithms whose goal is to learn the patterns of interaction, flow of information, or propagation of effects in social and information systems. We also welcome empirical studies, particularly attempts to bridge observational methods and causal inference, and studies that combine learning, networks, and computational social science. We are also interested in research that unifies the study of both structure and content in rich network datasets.

While this research field is already broad and diverse, there are emerging signs of convergence, maturation, and increased methodological awareness. For example, in the study of information diffusion, social media and social network researchers are beginning to use rigorous tools to distinguish effects driven by social influence, homophily, or external processes — subjects historically of intense interest amongst statisticians and social scientists. Similarly, there is a growing statistics literature developing learning approaches to study topics popularized earlier within the physics community, including clustering in graphs, network evolution, and random-graph models. Finally, learning methods are increasingly used in highly complex application domains, such as large-scale knowledge graph construction and use, and massive social networks like Facebook and LinkedIn. These applications are stimulating new scientific and practical questions that often cut across disciplinary boundaries.