Publications (new)
2022
Boekhout, H. D.; Heemskerk, E. M.; Takes, F. W.
Evolution of the World Stage of Global Science from a Scientific City Network Perspective Proceedings Article
In: Benito, R. M.; Cherifi, C.; Cherifi, Hocine; Moro, Esteban; Rocha, Luis M.; Sales-Pardo, Marta (Ed.): Complex Networks & Their Applications X, pp. 142–154, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93409-5.
Abstract | Links | BibTeX | Tags: City networks, Rank correlation, Scientific cities, Scientific co-authorship networks, Temporal networks
@inproceedings{boekhout_evolution_2022,
title = {Evolution of the World Stage of Global Science from a Scientific City Network Perspective},
author = {H. D. Boekhout and E. M. Heemskerk and F. W. Takes},
editor = {R. M. Benito and C. Cherifi and Hocine Cherifi and Esteban Moro and Luis M. Rocha and Marta Sales-Pardo},
doi = {10.1007/978-3-030-93409-5_13},
isbn = {978-3-030-93409-5},
year = {2022},
date = {2022-01-01},
booktitle = {Complex Networks & Their Applications X},
pages = {142–154},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {This paper investigates the stability and evolution of the world stage of global science at the city level by analyzing changes in co-authorship network centrality rankings over time. Driven by the problem that there exists no consensus in the literature on how the spatial unit “city” should be defined, we first propose a new approach to delineate so-called scientific cities. On a high-quality Web of Science dataset of 21.5 million publications over the period 2008–2020, we study changes in centrality rankings of subsequent 3-year time-slices of scientific city co-authorship networks at various levels of impact. We find that, over the years, the world stage of global science has become more stable. Additionally, by means of a comparison with degree respecting rewired networks we reveal how new co-authorships between authors from previously unconnected cities more often connect ‘close’ cities in the network periphery.},
keywords = {City networks, Rank correlation, Scientific cities, Scientific co-authorship networks, Temporal networks},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Bruin, G. J.; Veenman, C. J.; Herik, H. J.; Takes, F. W.
Supervised temporal link prediction in large-scale real-world networks Journal Article
In: Social Network Analysis and Mining, vol. 11, no. 1, pp. 80, 2021, ISSN: 1869-5469.
Abstract | Links | BibTeX | Tags: Multigraphs, Network evolution, Supervised learning, Temporal link prediction, Temporal networks
@article{de_bruin_supervised_2021,
title = {Supervised temporal link prediction in large-scale real-world networks},
author = {G. J. Bruin and C. J. Veenman and H. J. Herik and F. W. Takes},
url = {https://doi.org/10.1007/s13278-021-00787-3},
doi = {10.1007/s13278-021-00787-3},
issn = {1869-5469},
year = {2021},
date = {2021-08-01},
urldate = {2024-04-08},
journal = {Social Network Analysis and Mining},
volume = {11},
number = {1},
pages = {80},
abstract = {Link prediction is a well-studied technique for inferring the missing edges between two nodes in some static representation of a network. In modern day social networks, the timestamps associated with each link can be used to predict future links between so-far unconnected nodes. In these so-called temporal networks, we speak of temporal link prediction. This paper presents a systematic investigation of supervised temporal link prediction on 26 temporal, structurally diverse, real-world networks ranging from thousands to a million nodes and links. We analyse the relation between global structural properties of each network and the obtained temporal link prediction performance, employing a set of well-established topological features commonly used in the link prediction literature. We report on four contributions. First, using temporal information, an improvement of prediction performance is observed. Second, our experiments show that degree disassortative networks perform better in temporal link prediction than assortative networks. Third, we present a new approach to investigate the distinction between networks modelling discrete events and networks modelling persistent relations. Unlike earlier work, our approach utilises information on all past events in a systematic way, resulting in substantially higher link prediction performance. Fourth, we report on the influence of the temporal activity of the node or the edge on the link prediction performance, and show that the performance differs depending on the considered network type. In the studied information networks, temporal information on the node appears most important. The findings in this paper demonstrate how link prediction can effectively be improved in temporal networks, explicitly taking into account the type of connectivity modelled by the temporal edge. More generally, the findings contribute to a better understanding of the mechanisms behind the evolution of networks.},
keywords = {Multigraphs, Network evolution, Supervised learning, Temporal link prediction, Temporal networks},
pubstate = {published},
tppubtype = {article}
}
Mattsson, C. E. S.; Takes, F. W.
Trajectories through temporal networks Journal Article
In: Applied Network Science, vol. 6, no. 1, pp. 35, 2021, ISSN: 2364-8228.
Abstract | Links | BibTeX | Tags: Association football, Mobile money, Payment systems, Process-driven, Soccer, Temporal networks, Time-respecting paths, Trajectories, Walk processes
@article{mattsson_trajectories_2021,
title = {Trajectories through temporal networks},
author = {C. E. S. Mattsson and F. W. Takes},
url = {https://doi.org/10.1007/s41109-021-00374-7},
doi = {10.1007/s41109-021-00374-7},
issn = {2364-8228},
year = {2021},
date = {2021-05-01},
urldate = {2024-04-08},
journal = {Applied Network Science},
volume = {6},
number = {1},
pages = {35},
abstract = {What do football passes and financial transactions have in common? Both are networked walk processes that we can observe, where records take the form of timestamped events that move something tangible from one node to another. Here we propose an approach to analyze this type of data that extracts the actual trajectories taken by the tangible items involved. The main advantage of analyzing the resulting trajectories compared to using, e.g., existing temporal network analysis techniques, is that sequential, temporal, and domain-specific aspects of the process are respected and retained. As a result, the approach lets us produce contextually-relevant insights. Demonstrating the usefulness of this technique, we consider passing play within association football matches (an unweighted process) and e-money transacted within a mobile money system (a weighted process). Proponents and providers of mobile money care to know how these systems are used—using trajectory extraction we find that 73% of e-money was used for stand-alone tasks and only 21.7% of account holders built up substantial savings at some point during a 6-month period. Coaches of football teams and sports analysts are interested in strategies of play that are advantageous. Trajectory extraction allows us to replicate classic results from sports science on data from the 2018 FIFA World Cup. Moreover, we are able to distinguish teams that consistently exhibited complex, multi-player dynamics of play during the 2017–2018 club season using ball passing trajectories, coincidentally identifying the winners of the five most competitive first-tier domestic leagues in Europe.},
keywords = {Association football, Mobile money, Payment systems, Process-driven, Soccer, Temporal networks, Time-respecting paths, Trajectories, Walk processes},
pubstate = {published},
tppubtype = {article}
}