Abstract
The robustness of a network is determined by how well its vertices are connected to one another so as to keep the network strong and sustainable. As the network evolves, its robustness changes and may reveal events as well as periodic trend patterns that affect the interactions among users in the network. In this paper, we develop R-energy as a new measure of network robustness based on the spectral analysis of normalized Laplacian matrix. R-energy can cope with disconnected networks, and is efficient to compute with a time complexity of O(|V| + |E|) where V and E are the vertex set and edge set of the network respectively. This makes R-energy more efficient to compute than algebraic connectivity, another well known network robustness measure. Our experiments also show that removal of high degree vertices reduces network robustness (measured by R-energy) more than that of random or small degree vertices. R-energy can scale well for very large networks. It takes as little as 40 seconds to compute for a network with about 5M vertices and 69M edges. We can further detect events occurring in a dynamic Twitter network with about 130K users and discover interesting weekly tweeting trends by tracking changes to R-energy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.