Modern technology, including the World Wide Web, telecommunication devices and services, and large-scale data storage, has completely transformed the scale and concept of data in the sciences. Modern data sets are often enormous in size, detail, and heterogeneity, and 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 the analysis of networks is a growing and highly cross-disciplinary field.

This workshop aims to bring together a diverse and cross-disciplinary set of researchers in order to both describe recent advances and to discuss 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 economic systems. We will also welcome empirical studies in applied domains such as the social sciences, biology, medicine, neuroscience, physics, finance, social media, and economics.

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.

Goals:
The workshop's primary goal is to further facilitate the technical maturation of network analysis, promote greater technical sophistication and practical relevance, and identify future directions of research. To accomplish this, this workshop will 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 goals.

The NIPS community serves as the perfect middle ground to enable effective communication of both applied and methodological concerns. We aim to once again bring together a diverse set of researchers to assess progress and stimulate further debate in an ongoing, open, cross-disciplinary dialogue. We believe this effort will ultimately result both in novel modeling approaches, and in the identification of new applications and open problems that may serve as guidance for future research directions.

We welcome the following types of papers:

1. Research papers that introduce new models or apply established models to novel domains,

2. Research papers that explore theoretical and computational issues, or

3. Position papers that discuss shortcomings and desiderata of current approaches, or propose new directions for future research.

We encourage authors to emphasize the role of learning and its relevance to the application domains at hand. In addition, we hope to identify current successes in the area, and will therefore consider papers that apply previously proposed models to novel domains and data sets.