The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability

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Abstract

Our ability to generate, collect, and archive data related to everyday activities, such as interacting on social media, browsing the Web, and monitoring well-being, is rapidly increasing. Getting the most benefit from this large-scale data requires analysis of patterns it contains, which is computationally intensive or even intractable. Summarization techniques produce compact data representations (summaries) that enable faster processing by complex algorithms and queries. This talk will cover summarization of interconnected data (graphs) [1], which can represent a variety of natural processes (e.g., friendships, communication). I will present an overview of my group’s work on bridging the gap between research on summarized network representations and real-world problems. Examples include summarization of massive knowledge graphs for refinement [2] and on-device querying [3], summarization of graph streams for persistent activity detection [4], and summarization within graph neural networks for fast, interpretable classification [5]. I will conclude with open challenges and opportunities for future research.