Mining Temporal Networks
A Tutorial at the ACM Web Conference 2024
In World Wide Web (WWW) systems, networks (or graphs) serve as a fundamental tool for representing, analyzing, and understanding linked data, providing significant insights into the underlying systems. Naturally, most real-world systems have inherent temporal information, e.g., interactions in social networks occur at specific moments in time and last for a certain period. Temporal networks, i.e., network data modeling temporal information, enable novel and fundamental discoveries about the underlying systems they model, otherwise not captured by the static network model (i.e., networks not considering such temporal information).
In this tutorial, we present state-of-the-art models and algorithmic techniques for mining temporal networks that can provide precious insights into a plethora of web-related applications. We describe how temporal networks can be used to extract novel information, especially in web-related network data, and highlight the discoveries (and challenges) that arise when modeling temporal information compared to traditional static network-based approaches. We first overview different temporal network models. We then show how such powerful models can be leveraged to extract novel insights through suitable mining primitives. In particular, we present recent advances addressing most foundational problems for temporal network mining—ranging from the computation of temporal centrality measures, temporal motif counting, and temporal communities to bursty events and anomaly detection.
Previous Editions.
Tutorial Content
Part I: Introduction and Motivation
- Motivation and application areas.
- Main definitions and different types of temporal networks
- Models of temporal networks
- Algorithmic approaches
Part II: Mining temporal networks A
- Connectivity, temporal walks and paths, reachability
- Temporal properties
- Centrality measures
- Temporal cores decomposition
Part III: Mining temporal networks B
- Temporal communities
- Temporal patterns: trees, motifs, etc.
- Event detection and structural prediction
- Diffusion and spreading
- Synthetic and random network
Part IV: Tools and code libraries
Part V: Challenges, open problems, and trends
Slides
Tutors
Aristides Gionis
Aristides Gionis is a professor in the department of Division of Theoretical Computer Science at KTH Royal Institute of Technology in Stockholm, Sweden. He has been a fellow in the ISI foundation, Turin, and a visiting professor in the University of Rome. His previous appointment was with Yahoo! Research, Barcelona, where he has been a senior research scientist and group leader. He obtained his PhD in 2003 from Stanford University, USA. He is currently serving as an action editor in the Data Management and Knowledge Discovery journal (DMKD), and an associate editor in the ACM Transactions on the Web (TWEB). He has contributed in several areas of data science, such as algorithmic data analysis, web mining, social-media analysis, data clustering, and privacy-preserving data mining. His current research is funded by by the European Commission with an ERC Advanced grant (REBOUND) and with RIA project SoBigData++, and by the Wallenberg AI, Autonomous Systems and Software Program (WASP) in Sweden. Aristides Gionis has presented several tutorials on graph mining and web mining in conferences such as the Web Conference (2008, 2018, 2020, 2021, and 2022), KDD (2013, 2015, 2018, and 2019), ECML PKDD (2008, 2013, and 2015), IJCAI (2011 and 2022), as well as in many summer schools, including the EDBT Summer School 2019 and the Hi! Paris Data Science Summer School 2022.
Lutz Oettershagen
Lutz Oettershagen is a postdoctoral researcher in the Division of Theoretical Computer Science at KTH Royal Institute of Technology in Stockholm, Sweden. He has been a postdoctoral researcher at the University of Bonn in Germany, where he also obtained his PhD. He obtained his master’s degree at the TU University of Dortmund. His main research interests are algorithmic data analysis and data mining on temporal networks, focusing on social networks. In recent works, he covers temporal centrality measures, community search, and tie strength inference in temporal networks.
Ilie Sarpe
Ilie Sarpe is a postdoctoral researcher in the Division of Theoretical Computer Science at KTH Royal Institute of Technology in Stockholm, Sweden. He obtained his PhD in Information Engineering from the University of Padova, Italy where he also earned his MSc in 2023 and 2019 respectively. His research interests focus on the development of scalable algorithms with rigorous theoretical guarantees for data mining primitives, and randomized algorithms. In particular, recently he focused on efficient algorithms with probabilistic guarantees for problems related to collecting patterns and centrality measures in temporal networks.
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Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023.
10.1145/3580305.3599242 - On Computing Large Temporal (Unilateral) Connected Components.
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10.1137/1.9781611977653.ch32 - Temporal Network Core Decomposition and Community Search.
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arXiv preprint arXiv:2309.11843. 2023.
10.1007/978-3-030-59728-3_37 - A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks.
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10.1137/1.9781611977653.ch3 - Densest Periodic Subgraph Mining on Large Temporal Graphs.
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IEEE Transactions on Knowledge and Data Engineering. 2023.
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