Summarizing Graphs at Multiple Scales: New Trends

Abstract

Recent advances in computing resources have made it possible to collect enormous amounts of interconnected data, such as social media interactions, web activity, knowledge bases, product and service purchases, autonomous vehicle routing, smart home sensor data, and more. The massive scale and complexity of this data, however, not only vastly surpasses human processing power, but also goes beyond limitations with regard to computation and storage. That is, there is an urgent need for methods and tools that summarize large interconnected data to enable faster computations, storage reduction, interactive large-scale visualization and understanding, and pattern discovery. Network summarization-which aims to find a small representation of an original, larger graph-features a variety of methods with different goals and for different input data representations (e.g., attributed graphs, time-evolving or streaming graphs, heterogeneous graphs). The objective of this tutorial is to give a systematic overview of methods for summarizing and explaining graphs at different scales: the node-group level, the network level, and the multi-network level. We emphasize the current challenges, present real-world applications, and highlight the open research problems in this vibrant research area.