NDA 2016 - International Workshop on Network Data Analytics (NDA) 2016

Networks are prevalent in today’s electronic world in a wide variety of domains ranging from Engineering to Social Sciences, Life Sciences to Data Analytics and so on. Researchers and practitioners have studied networks in multiple ways like defining network metrics, providing theoretical results and examining problems like pattern mining, link prediction etc. Recently, we have witnessed proliferation of networks in new business domains like Telecommunications, Banking, Retail, Healthcare etc. Most of these real-world applications give rise to networks which exhibit unique and interesting structures supporting multiple dynamical processes that shape these networks over time. Owing to the tremendous pace of growth of electronic data many of these networks are also evolving at a rapid pace leading to evolving networks.
Graphs are a popular representation for such data because of their ability to represent different entity and relationship types, including the temporal relationships necessary to represent the dynamics of a data stream. However, fusing such heterogeneous data into a single graph or multiple related graphs and mining is challenging task. Emerging massive data has made calls for fundamental change to graph data modelling and programming paradigm. APACHE SPARK is one such successful instantiation. Finally, it is interesting to see the applicability of graph based techniques by applying them to even wider range of data like spatial, spatio-temporal and IOT data which did not inherently exhibit network structure by modelling relationships.
This workshop is a forum for exchanging ideas and methods for mining and learning with networks, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances graph analysis. Towards that we would like to encourage applications and demonstrations of relevant real-life systems and research prototypes.