When a network is inferred from data, two types of errors can occur: false positive and false negative conclusions about the presence of links. We focus on the influence of local network characteristics on the probability α of false positive conclusions, and on the probability β of false negative conclusions, in the case of networks of coupled oscillators. We demonstrate that false conclusion probabilities are influenced by local connectivity measures such as the shortest path length and the detour degree, which can also be estimated from the inferred network when the true underlying network is not known a priori. These measures can then be used for quantification of the confidence level of link conclusions, and for improving the network re...
In real life, the actual topology of a network is often difficult to observe or even unobservable, w...
In this paper, we present a method that combines information-theoretical and statistical approaches ...
The human brain is a complex network of anatomically segregated regions interconnected by white matt...
This project has received funding from the European Union's Horizon 2020 research and innovation pro...
Acknowledgements This project has received funding from the European Union’s Horizon 2020 research a...
In various applications involving complex networks, network measures are employed to assess the rela...
Modeling and analysis of imperfection in network data is essential in many applications such as prot...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
International audienceThe application of Graph Theory to the brain connectivity patterns obtained fr...
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. Understanding the me...
<p><b>A</b> shows the original network. <b>B</b> shows the network inferred with our new method from...
<p>True −− network metrics indicated where appropriate. The dotted line ⋯ shows the level of the fal...
In this paper we discuss why a simple network topology inference algorithm based on network co-occur...
International audienceLink prediction in networks works better when those networks are connected and...
In real life, the actual topology of a network is often difficult to observe or even unobservable, w...
In this paper, we present a method that combines information-theoretical and statistical approaches ...
The human brain is a complex network of anatomically segregated regions interconnected by white matt...
This project has received funding from the European Union's Horizon 2020 research and innovation pro...
Acknowledgements This project has received funding from the European Union’s Horizon 2020 research a...
In various applications involving complex networks, network measures are employed to assess the rela...
Modeling and analysis of imperfection in network data is essential in many applications such as prot...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
International audienceThe application of Graph Theory to the brain connectivity patterns obtained fr...
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. Understanding the me...
<p><b>A</b> shows the original network. <b>B</b> shows the network inferred with our new method from...
<p>True −− network metrics indicated where appropriate. The dotted line ⋯ shows the level of the fal...
In this paper we discuss why a simple network topology inference algorithm based on network co-occur...
International audienceLink prediction in networks works better when those networks are connected and...
In real life, the actual topology of a network is often difficult to observe or even unobservable, w...
In this paper, we present a method that combines information-theoretical and statistical approaches ...
The human brain is a complex network of anatomically segregated regions interconnected by white matt...