
Network Growth From Global and Local Influential Nodes
In graph theory and network analysis, node degree is defined as a simple...
read it

Node Overlap Removal Algorithms: A Comparative Study
Many algorithms have been designed to remove node overlapping, and many ...
read it

On the Size of the Giant Component in Inhomogeneous Random Kout Graphs
Inhomogeneous random Kout graphs were recently introduced to model hete...
read it

Limits and tradeoffs of topological network robustness
We investigate the tradeoff between the robustness against random and t...
read it

Distributionally Robust Removal of Malicious Nodes from Networks
An important problem in networked systems is detection and removal of su...
read it

Robustness and stability of enterprise intranet social networks: The impact of moderators
In this study, we tested the robustness of three communication networks ...
read it

A CoverageAware Distributed kConnectivity Maintenance Algorithm for Arbitrarily Large k in Mobile Sensor Networks
Mobile sensor networks (MSNs) have emerged from the interaction between ...
read it
Network connectivity under a probabilistic node failure model
Centrality metrics have been widely applied to identify the nodes in a graph whose removal is effective in decomposing the graph into smaller subcomponents. The node–removal process is generally used to test network robustness against failures. Most of the available studies assume that the node removal task is always successful. Yet, we argue that this assumption is unrealistic. Indeed, the removal process should take into account also the strength of the targeted node itself, to simulate the failure scenarios in a more effective and realistic fashion. Unlike previous literature, herein a probabilistic node failure model is proposed, in which nodes may fail with a particular probability, considering two variants, namely: Uniform (in which the nodes survivaltofailure probability is fixed) and Best Connected (BC) (where the nodes survival probability is proportional to their degree). To evaluate our method, we consider five popular centrality metrics carrying out an experimental, comparative analysis to evaluate them in terms of effectiveness and coverage, on four realworld graphs. By effectiveness and coverage we mean the ability of selecting nodes whose removal decreases graph connectivity the most. Specifically, the graph spectral radius reduction works as a proxy indicator of effectiveness, and the reduction of the largest connected component (LCC) size is a parameter to assess coverage. The metric that caused the biggest drop has been then compared with the Benchmark analysis (i.e, the nonprobabilistic degree centrality node removal process) to compare the two approaches. The main finding has been that significant differences emerged through this comparison with a deviation range that varies from 2% up to 80% regardless of the dataset used that highlight the existence of a gap between the common practice with a more realistic approach.
READ FULL TEXT
Comments
There are no comments yet.