Aims and Scope

The aim of this workshop called Large-Scale Time Dependent Graphs (TD-LSG) 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 time-dependent graphs, the graphs that have this spatiotemporal feature.

In this workshop, we aim to discuss the problem of mining large-scale time-dependent graphs, since there are many real world applications deal with a large volumes of spatio-temporal data (e.g. moving objects’ trajectories). Managing and analysing large-scale time-dependent 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 large-scale graphs.

Workshop topics

We 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 inter-linked topics, with regards to mining process:

• Theoretical foundation of TD-LSG
• Construction and maintenance of TD-LSG
• Data quality in TD-LSG
• Data integration in TD-LSG
• Indexing techniques for TD-LSG
• Distributed algorithms & navigational query processing
• TD-LSG data mining: frequent pattern mining, similarity, cluster analysis, predictive learning
• Trajectory mining in TD-LSG
• Probabilistic TD-LSG
• Applications related to TD-LSG