Recently, statistics and machine learning have seen the proliferation of both theoretical and computational tools for analyzing graphs to support progress in applied domains such as social sciences, biology, medicine, neuroscience, physics, finance, and economics. This workshop actively promotes a concerted effort to address statistical, methodological and computational issues that arise when modeling and analyzing large collection of data which are primarily represented as static and/or dynamic graphs. Presentations include (but are not limited to) novel graph models, the application of established models to new domains, theoretical and computational issues, limitations of current graph methods and directions for future research. The workshop aims to bring together researchers from applied disciplines such as sociology, economics, medicine and biology with researchers from mathematics, physics, statistics and computer science.