Important dates:
 Abstract Submission Deadline: Monday, July 10, 2017 (extended)
 Paper Submission Deadline: Monday, July 10, 2017 (extended)
 Author Notification: Monday, July 24, 2017
 Camera Ready Deadline: Monday, August 7, 2017
Useful links:
Aims and ScopeThe aim of this workshop called LargeScale Time Dependent Graphs (TDLSG) is to bring together active scholars and practitioners of dynamic graphs. Graph models and algorithms are ubiquitous of a large number of application domains, ranging from transportation to social networks, semantic web, or data mining. However, many applications require graph models that are time dependent. For example, applications related to urban mobility analysis employ a graph structure of the underlying road network. Indeed, the nature of such networks are spatiotemporal. Therefore, the time a moving object takes to cross a path segment typically depends on the starting instant of time. So, we call timedependent graphs, the graphs that have this spatiotemporal feature. In this workshop, we aim to discuss the problem of mining largescale timedependent graphs, since there are many real world applications deal with a large volumes of spatiotemporal data (e.g. moving objectsâ€™ trajectories). Managing and analysing largescale timedependent graphs is very challenging since this requires sophisticated methods and techniques for creating, storing, accessing and processing such graphs in a distributed environment, because centralized approaches do not scale in a Big Data scenario. Contributions will clearly point out answers to one of these challenges focusing on largescale graphs. Workshop topicsWe encourage papers with important new insights and experiences on knowledge discovery aspects with dynamic and evolving graphs. Those contributions should shed light on one of the questions mentioned above, related to the knowledge discovery process. Topics of interest include, but are not limited to, the following interlinked topics, with regards to mining process:
