The Role of Network Size for the Robustness of Centrality Measures
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Complex Networks and Their Applications VIII: Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. ed. / Hocine Cherifi; Sabrina Gaito; Jose Fernendo Mendes; Esteban Moro; Luis Mateus Rocha. Vol. 1 Cham: Springer Schweiz, 2020. p. 40-51 (Studies in Computational Intelligence; Vol. 881).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - The Role of Network Size for the Robustness of Centrality Measures
AU - Martin, Christoph
AU - Niemeyer, Peter
N1 - Conference code: 8
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Measurement errors are omnipresent in network data. Studies have shown that these errors have a severe impact on the robustness of centrality measures. It has been observed that the robustness mainly depends on the network structure, the centrality measure, and the type of error. Previous findings regarding the influence of network size on robustness are, however, inconclusive. Based on twenty-four empirical networks, we investigate the relationship between global network measures, especially network size and average degree, and the robustness of the degree, eigenvector centrality, and PageRank. We demonstrate that, in the vast majority of cases, networks with a higher average degree are more robust. For random graphs, we observe that the robustness of Erdős-Rényi (ER) networks decreases with an increasing average degree, whereas with Barabàsi-Albert networks, the opposite effect occurs: with an increasing average degree, the robustness also increases. As a first step into an analytical discussion, we prove that for ER networks of different size but with the same average degree, the robustness of the degree centrality remains stable.
AB - Measurement errors are omnipresent in network data. Studies have shown that these errors have a severe impact on the robustness of centrality measures. It has been observed that the robustness mainly depends on the network structure, the centrality measure, and the type of error. Previous findings regarding the influence of network size on robustness are, however, inconclusive. Based on twenty-four empirical networks, we investigate the relationship between global network measures, especially network size and average degree, and the robustness of the degree, eigenvector centrality, and PageRank. We demonstrate that, in the vast majority of cases, networks with a higher average degree are more robust. For random graphs, we observe that the robustness of Erdős-Rényi (ER) networks decreases with an increasing average degree, whereas with Barabàsi-Albert networks, the opposite effect occurs: with an increasing average degree, the robustness also increases. As a first step into an analytical discussion, we prove that for ER networks of different size but with the same average degree, the robustness of the degree centrality remains stable.
KW - Centrality
KW - Measurement error
KW - Missing data
KW - Noisy data
KW - Robustness
KW - Sampling
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85076713879&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36687-2_4
DO - 10.1007/978-3-030-36687-2_4
M3 - Article in conference proceedings
AN - SCOPUS:85076713879
SN - 978-3-030-36686-5
VL - 1
T3 - Studies in Computational Intelligence
SP - 40
EP - 51
BT - Complex Networks and Their Applications VIII
A2 - Cherifi, Hocine
A2 - Gaito, Sabrina
A2 - Mendes, Jose Fernendo
A2 - Moro, Esteban
A2 - Rocha, Luis Mateus
PB - Springer Schweiz
CY - Cham
T2 - International Conference on Complex Networks and their Applications - 2019
Y2 - 10 December 2019 through 12 December 2019
ER -