TY - GEN
T1 - Ricci curvature of the Internet topology
AU - Ni, Chien Chun
AU - Lin, Yu Yao
AU - Gao, Jie
AU - David Gu, Xianfeng
AU - Saucan, Emil
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/8/21
Y1 - 2015/8/21
N2 - Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's 'thin triangle condition', which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier [1], Lin et al. [2], etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.
AB - Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's 'thin triangle condition', which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier [1], Lin et al. [2], etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.
UR - https://www.scopus.com/pages/publications/84954478055
U2 - 10.1109/INFOCOM.2015.7218668
DO - 10.1109/INFOCOM.2015.7218668
M3 - Conference contribution
T3 - Proceedings - IEEE INFOCOM
SP - 2758
EP - 2766
BT - 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Y2 - 26 April 2015 through 1 May 2015
ER -