TY - JOUR
T1 - Reconstructing irreducible links in temporal networks
T2 - Which tool to choose depends on the network size
AU - Nadini, Matthieu
AU - Rizzo, Alessandro
AU - Porfiri, Maurizio
N1 - Funding Information:
The authors acknowledges financial support from the National Science Foundation under Grant No. CMMI-1561134. AR acknowledges financial support from Compagnia di San Paolo, Italy, and the Italian Ministry of Foreign Affairs and International Cooperation, within the project ‘Mac2Mic’, ‘Macro to Micro: uncovering the hidden mechanisms driving network dynamics’.
Publisher Copyright:
© 2020 The Author(s). Published by IOP Publishing Ltd
PY - 2020/5/29
Y1 - 2020/5/29
N2 - Filtering information in complex networks entails the process of removing interactions explained by a proper null hypothesis and retaining the remaining interactions, which form the backbone network. The reconstructed backbone network depends upon the accuracy and reliability of the available tools, which, in turn, are affected by the specific features of the available dataset. Here, we examine the performance of three approaches for the discovery of backbone networks, in the presence of heterogeneous, time-varying node properties. In addition to the recently proposed evolving activity driven model, we extend two existing approaches (the disparity filter and the temporal fitness model) to tackle time-varying phenomena. Our analysis focuses on the influence of the network size, which was previously shown to be a determining factor for the performance of the evolving activity driven model. Through mathematical and numerical analysis, we propose general guidelines for the use of these three approaches based on the available dataset. For small networks, the evolving temporal fitness model offers a more reasonable trade-off between the number of links assigned to the backbone network and the accuracy of their inference. The main limitation of this methodology lies in its computational cost, which becomes excessively high for large networks. In this case, the evolving activity driven model could be a valid substitute to the evolving temporal fitness model. If one seeks to minimize the number of links inaccurately included in the backbone network at the risk of dismissing many links that could belong to it, then the temporal disparity filter would be the approach-of-choice. Overall, our contribution expands the toolbox of network discovery in the technical literature and should help users in choosing the right network discovery instrument, depending on the problem considered.
AB - Filtering information in complex networks entails the process of removing interactions explained by a proper null hypothesis and retaining the remaining interactions, which form the backbone network. The reconstructed backbone network depends upon the accuracy and reliability of the available tools, which, in turn, are affected by the specific features of the available dataset. Here, we examine the performance of three approaches for the discovery of backbone networks, in the presence of heterogeneous, time-varying node properties. In addition to the recently proposed evolving activity driven model, we extend two existing approaches (the disparity filter and the temporal fitness model) to tackle time-varying phenomena. Our analysis focuses on the influence of the network size, which was previously shown to be a determining factor for the performance of the evolving activity driven model. Through mathematical and numerical analysis, we propose general guidelines for the use of these three approaches based on the available dataset. For small networks, the evolving temporal fitness model offers a more reasonable trade-off between the number of links assigned to the backbone network and the accuracy of their inference. The main limitation of this methodology lies in its computational cost, which becomes excessively high for large networks. In this case, the evolving activity driven model could be a valid substitute to the evolving temporal fitness model. If one seeks to minimize the number of links inaccurately included in the backbone network at the risk of dismissing many links that could belong to it, then the temporal disparity filter would be the approach-of-choice. Overall, our contribution expands the toolbox of network discovery in the technical literature and should help users in choosing the right network discovery instrument, depending on the problem considered.
KW - Activity
KW - Backbone network
KW - Disparity filter
KW - Fitness model
KW - Statistical filtering
KW - Time-varying
UR - http://www.scopus.com/inward/record.url?scp=85099897504&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099897504&partnerID=8YFLogxK
U2 - 10.1088/2632-072X/ab6727
DO - 10.1088/2632-072X/ab6727
M3 - Article
AN - SCOPUS:85099897504
SN - 2632-072X
VL - 1
JO - Journal of Physics: Complexity
JF - Journal of Physics: Complexity
IS - 1
M1 - 015001
ER -