# BMNR: Design and Implementation a Benchmark for Metrics of Network Robustness

Title | BMNR: Design and Implementation a Benchmark for Metrics of Network Robustness |

Publication Type | Conference Paper |

Year of Publication | 2017 |

Authors | Zheng, J., Li, Y., Hou, Y., Gao, M., Zhou, A. |

Conference Name | 2017 IEEE International Conference on Big Knowledge (ICBK) |

Date Published | aug |

Keywords | big data security metrics, BMNR, Conferences, graph attack, graph generator, graph theory, Hybrid electric vehicles, Metrics, network robustness, pubcrawl, Resiliency, robustness metric evaluation, Scalability, social networking (online) |

Abstract | The network robustness is defined by how well its vertices are connected to each other to keep the network strong and sustainable. The change of network robustness may reveal events as well as periodic trend patterns that affect the interactions among vertices in the network. The evaluation of network robustness may be helpful to many applications, such as event detection, disease transmission, and network security, etc. There are many existing metrics to evaluate the robustness of networks, for example, node connectivity, edge connectivity, algebraic connectivity, graph expansion, R-energy, and so on. It is a natural and urgent problem how to choose a reasonable metric to effectively measure and evaluate the network robustness in the real applications. In this paper, based on some general principles, we design and implement a benchmark, namely BMNR, for the metrics of network robustness. The benchmark consists of graph generator, graph attack and robustness metric evaluation. We find that R-energy can evaluate both connected and disconnected graphs, and can be computed more efficiently. |

URL | http://ieeexplore.ieee.org/document/8023437/ |

DOI | 10.1109/ICBK.2017.58 |

Citation Key | zheng_bmnr:_2017 |