Instructors

Motivation

Networks (or graphs) are used to represent and analyze large datasets of objects and their relations. Typical examples of graph applications come from social networks, traffic networks, electric power grids, road systems, the Internet, chemical and biological systems, and more. Naturally, real-world networks have a temporal component: for instance, interactions between objects have a timestamp and a duration. In this tutorial we present models and algorithms for mining temporal networks, i.e., network data with temporal information. We overview different models used to represent temporal networks. We highlight the main differences between static and temporal networks, and discuss the challenges arising from introducing the temporal dimension in the network representation. We present recent papers addressing the most well-studied problems in the setting of temporal networks, including computation of centrality measures, motif detection and counting, community detection and monitoring, event and anomaly detection, analysis of epidemic processes and influence spreading, network summarization, and structure prediction. Finally, we discuss some of the current challenges and open problems in the area, and we highlight directions for future work.

EDBT19 summer school tutorial slides

KDD19 tutorial slides

Key surveys, definitions, and applications

Models of temporal networks

Network measures: characterization and computation

Algorithmic approaches

Data Mining problems