The increasing interest in social networks, knowledge graphs, protein-interaction, and many other types of networks has raised the question how users can explore such large and complex graph structures easily. Current tools focus on graph management, graph mining, or graph visualization but lack user-driven methods for graph exploration.In many cases graph methods try to scale to the size and complexity of a real network. However, methods miss user requirements such as exploratory graph query processing, intuitive graph explanation, and interactivity in graph exploration. While there is consensus in database and data mining communities on the definition of data exploration practices for relational and semi-structured data, graph exploration practices are still indeterminate.
In this tutorial, we will discuss a set of techniques, which have been developed in the last few years for independent purposes, within a unified graph exploration taxonomy. The tutorial will provide a generalized definition of graph exploration in which the user interacts directly with the system either providing feedback or a partial query. We will discuss common, diverse, and missing properties of graph exploration techniques based on this definition, our taxonomy, and multiple applications for graph exploration. Concluding this discussion we will highlight interesting and relevant challenges for data scientists in graph exploration.
Outline and material
- Introduction and data exploration taxonomy
- User-driven Graph Exploration
- [First part] Exploratory Graph Analysis
- [Second part] Refinement of Graph Query Results
- [Third part] Focused Graph Mining
- Machine learning for Graph Exploraiton
- Open Challenges
Davide Mottin is a postdoctoral researcher at Hasso Plattner Institute and GFZ center in Potsdam. Previously, he received his PhD in 2015 from the University of Trento. His research interests include graph mining, novel query paradigms, and interactive methods. He published in distinguished database and data mining conferences like VLDB, SIGMOD, ICDE, WWW and KDD and is actively actively engaged in teaching database, big data analytics, and graph mining for Bachelor and Master courses as well as projects involving companies and students. He is the proponent of exemplar queries paradigm for exploratory analysis.
Emmanuel Müller is professor and head of the Knowledge Discovery and Data Mining group at Hasso Plattner Institute. His research interests include graph mining, stream mining, clustering and outlier mining on graphs, streams, and traditional databases. He presented tutorials in database, data mining, and machine learning conferences such as SDM, ICDM, and ICML. He received his PhD in 2010 from RWTH Aachen University, had been independent group leader at Karlsruhe Institute of Technology (2010 - 2015) and postdoctoral fellow at University of Antwerp (2012 - 2015).