Problems involving networks and massive network datasets motivate some of the most difficult and exciting inferential challenges today, from social, economic, and biological domains. Modern network data are often more than just vertices and edges, containing rich information on vertex attributes, edge weights, and characteristics that change over time. Enormous in size, detail, and heterogeneity, these networks are often best represented as highly annotated sequences of graphs. Although much progress has been made on developing rigorous tools for analyzing and modeling some types of large, complex, real-world networks, much work still remains, and a principled, coherent framework remains elusive, in part because network analysis is a young and highly cross-disciplinary field.
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, biological, and informational systems. We also welcome empirical studies in applied domains such as the social sciences, biology, medicine, neuroscience, physics, finance, social media, and economics. And, we are particularly 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 brain networks, and massive social networks like Facebook, and these applications are stimulating new scientific and practical questions that sometimes cut across disciplinary boundaries.
The workshop's primary goal is to facilitate the technical maturation of network analysis, promote greater technical sophistication and practical relevance, and identify future directions of research. This workshop will thus bring together researchers from disciplines like computer science, statistics, physics, informatics, economics, sociology, with an emphasis on theoretical discussions of fundamental questions.
The technical focus of the workshop is the statistical, methodological and computational issues that arise when modeling and analyzing large collections of heterogeneous and potentially dynamic network data. We seek to foster cross-disciplinary collaborations and intellectual exchange between the different communities and their respective ideas and tools. The communities identified above have long-standing interest in network modeling, and we aim to explore the similarities and differences both in methods and in goals.
The NIPS community is well positioned as a middle ground for effective dialog between applied and methodological concerns. We aim to further leverage this position to facilitate an open, cross-disciplinary discussion among researchers to assess progress and stimulate further debate on networks. We believe these efforts will ultimately yield novel modeling approaches and the identification of new applications or open problems that will guide future research in networks.