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.