2016/06/28 by Jeremy F. Alm, Alm, Jeremy F., Keenan M. L. Mack +1
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · #05C80 #05C85 #92D30 #Advanced biosensing and bioanalysis techniques #Combinatorics (math.CO) #Complex Network Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Graph theory and applications #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1606.08768
openalex publication_date 2016/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many naturally occurring networks have a power-law degree distribution as\nwell as a non-zero degree correlation. Despite this, most studies analyzing the\nrobustness to random node-deletion and vulnerability to targeted node-deletion\nhave concentrated only on power-law degree distribution and ignored degree\ncorrelation. This study looks specifically at the effect degree-correlation has\non robustness and vulnerability in scale-free networks. Our results confirm\nNewman's finding that positive degree-correlation increases robustness and\ndecreases vulnerability. However, we found that networks with positive\ndegree-correlation are more vulnerable to random node-deletion than to targeted\ndeletion methods that utilize knowledge of initial node-degree only. Targeted\ndeletion sufficiently alters the topology of the network to render this method\nless effective than uniform random methods unless changes in topology are\naccounted for. This result indicates the importance of degree correlation in\ncertain network applications.\n