4 research outputs found
Information Theory Perspective on Network Robustness
A crucial challenge in network theory is the study of the robustness of a
network after facing a sequence of failures. In this work, we propose a
dynamical definition of network's robustness based on Information Theory, that
considers measurements of the structural changes caused by failures of the
network's components. Failures are defined here, as a temporal process defined
in a sequence. The robustness of the network is then evaluated by measuring
dissimilarities between topologies after each time step of the sequence,
providing a dynamical information about the topological damage. We thoroughly
analyze the efficiency of the method in capturing small perturbations by
considering both, the degree and distance distributions. We found the network's
distance distribution more consistent in capturing network structural
deviations, as better reflects the consequences of the failures. Theoretical
examples and real networks are used to study the performance of this
methodology.Comment: 5 pages, 2 figures, submitte
Quantification of network structural dissimilarities
Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components
Quantification of network structural dissimilarities
Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components