Publications (new)
2023
Jong, R. G.; Loo, M. P. J.; Takes, F. W.
Algorithms for Efficiently Computing Structural Anonymity in Complex Networks Journal Article
In: ACM Journal of Experimental Algorithmics, vol. 28, pp. 1.7:1–1.7:22, 2023, ISSN: 1084-6654.
Abstract | Links | BibTeX | Tags: anonymity, Complex networks, graph algorithms, privacy
@article{de_jong_algorithms_2023,
title = {Algorithms for Efficiently Computing Structural Anonymity in Complex Networks},
author = {R. G. Jong and M. P. J. Loo and F. W. Takes},
url = {https://dl.acm.org/doi/10.1145/3604908},
doi = {10.1145/3604908},
issn = {1084-6654},
year = {2023},
date = {2023-08-01},
urldate = {2024-04-08},
journal = {ACM Journal of Experimental Algorithmics},
volume = {28},
pages = {1.7:1–1.7:22},
abstract = {This article proposes methods for efficiently computing the anonymity of entities in networks. We do so by partitioning nodes into equivalence classes where a node is k-anonymous if it is equivalent to k-1 other nodes. This assessment of anonymity is crucial when one wants to share data and must ensure the anonymity of entities represented is compliant with privacy laws. Additionally, in such an assessment, it is necessary to account for a realistic amount of information in the hands of a possible attacker that attempts to de-anonymize entities in the network. However, measures introduced in earlier work often assume a fixed amount of attacker knowledge. Therefore, in this work, we use a new parameterized measure for anonymity called d-k-anonymity. This measure can be used to model the scenario where an attacker has perfect knowledge of a node’s surroundings up to a given distance d. This poses nontrivial computational challenges, as naive approaches would employ large numbers of possibly computationally expensive graph isomorphism checks. This article proposes novel algorithms that severely reduce this computational burden. In particular, we present an iterative approach, assisted by techniques for preprocessing nodes that are trivially automorphic and heuristics that exploit graph invariants. We evaluate our algorithms on three well-known graph models and a wide range of empirical network datasets. Results show that our approaches significantly speed up the computation by multiple orders of magnitude, which allows one to compute d-k-anonymity for a range of meaningful values of d on large empirical networks with tens of thousands of nodes and over a million edges.},
keywords = {anonymity, Complex networks, graph algorithms, privacy},
pubstate = {published},
tppubtype = {article}
}
Bokányi, E.; Heemskerk, E. M.; Takes, F. W.
The anatomy of a population-scale social network Journal Article
In: Scientific Reports, vol. 13, no. 1, pp. 9209, 2023, ISSN: 2045-2322, (Publisher: Nature Publishing Group).
Abstract | Links | BibTeX | Tags: Complex networks, Socioeconomic scenarios
@article{bokanyi_anatomy_2023,
title = {The anatomy of a population-scale social network},
author = {E. Bokányi and E. M. Heemskerk and F. W. Takes},
url = {https://www.nature.com/articles/s41598-023-36324-9},
doi = {10.1038/s41598-023-36324-9},
issn = {2045-2322},
year = {2023},
date = {2023-06-01},
urldate = {2024-04-08},
journal = {Scientific Reports},
volume = {13},
number = {1},
pages = {9209},
abstract = {Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level.},
note = {Publisher: Nature Publishing Group},
keywords = {Complex networks, Socioeconomic scenarios},
pubstate = {published},
tppubtype = {article}
}
Mattsson, C. E. S.; Criscione, T.; Takes, F. W.
Circulation of a digital community currency Journal Article
In: Scientific Reports, vol. 13, no. 1, pp. 5864, 2023, ISSN: 2045-2322, (Publisher: Nature Publishing Group).
Abstract | Links | BibTeX | Tags: Complex networks, Computational science
@article{mattsson_circulation_2023,
title = {Circulation of a digital community currency},
author = {C. E. S. Mattsson and T. Criscione and F. W. Takes},
url = {https://www.nature.com/articles/s41598-023-33184-1},
doi = {10.1038/s41598-023-33184-1},
issn = {2045-2322},
year = {2023},
date = {2023-04-01},
urldate = {2024-04-08},
journal = {Scientific Reports},
volume = {13},
number = {1},
pages = {5864},
abstract = {Circulation is the characteristic feature of successful currency systems, from community currencies to cryptocurrencies to national currencies. In this paper, we propose a network analysis approach especially suited for studying circulation given a system’s digital transaction records. Sarafu is a digital community currency that was active in Kenya over a period that saw considerable economic disruption due to the COVID-19 pandemic. We represent its circulation as a network of monetary flow among the 40,000 Sarafu users. Network flow analysis reveals that circulation was highly modular, geographically localized, and occurring among users with diverse livelihoods. Across localized sub-populations, network cycle analysis supports the intuitive notion that circulation requires cycles. Moreover, the sub-networks underlying circulation are consistently degree disassortative and we find evidence of preferential attachment. Community-based institutions often take on the role of local hubs, and network centrality measures confirm the importance of early adopters and of women’s participation. This work demonstrates that networks of monetary flow enable the study of circulation within currency systems at a striking level of detail, and our findings can be used to inform the development of community currencies in marginalized areas.},
note = {Publisher: Nature Publishing Group},
keywords = {Complex networks, Computational science},
pubstate = {published},
tppubtype = {article}
}