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Abstract

A graph is a versatile data structure facilitating representation of interactions among objects in various complex systems. Very often these objects have attributes whose measurements change over time, reflecting the dynamics of the system. This general data framework can be used in many fields to represent complex data structures: brain networks and neuronal spikes, web networks and clickstreams, social networks and activity of the users, among others. In all of these examples, the structural and dynamic components of the data are inseparable, which significantly complicates the detection, analysis, and interpretation of patterns that emerge in the networks. The increasing size and complexity of graph-structured data require scalable and interpretable algorithms for dynamic pattern detection in such systems. In this dissertation, we present an unsupervised approach for dynamic pattern detection in large-scale graphs. In this approach, we combine intuitions derived from attention mechanisms, Hopfield networks, and memory networks to build scalable, efficient, and interpretable algorithms. We then demonstrate multiple applications of this approach in recommendation systems, information recovery algorithms, and collective behavior studies. Additionally, we use our algorithm to detect dynamic activity patterns in social and communication networks. We conduct extensive experiments on Wikipedia data, detecting and analyzing patterns in the viewership activity in its web network. To study the collective behavior of Wikipedia readers, we develop an automated pattern interpretation model, which allows for comparison of trending topics across multiple language editions of Wikipedia. The results of the experiments reveal provocative insights into how people interact and search for information in online social networking environments, opening new avenues for future research on collective behavior analysis at a large scale. Finally, we present a distributed data processing framework for Wikipedia server logs that allows others to reproduce all pattern detection experiments presented in this thesis and to conduct similar collective behavior studies on the latest data.

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