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### IDENTIFYING INFLUENTIAL SPREADERS IN ARTIFICIAL COMPLEX NETWORKS

WANG Pei, TIAN Chengeng , LU Jun-an

1. 1.School of Mathematics and Information Sciences, Henan University, Kaifeng 475004, China; 2. TIAN Chengeng · LU Jun-an (Corresponding author)
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
• Received:2012-11-05 Online:2014-08-25 Published:2014-08-19
• Supported by:

This research is supported by the National Natural Science Foundation of China under Grant Nos. 11172215, 61304151, 61174028, China-Australia Health and HIV/AIDS Facility (FA36 EID101), and the Science Foundation of Henan University under Grant No. 2012YBZR007.

WANG Pei, TIAN Chengeng , LU Jun-an. IDENTIFYING INFLUENTIAL SPREADERS IN ARTIFICIAL COMPLEX NETWORKS[J]. Journal of Systems Science and Complexity, 2014, 27(4): 650-665.

A long-term common belief in complex networks is that, the most connected nodes are the most efficient spreaders. However, recent investigations on real-world complex networks show that the most influential spreaders are those with the highest k-shell values. It is well-known that, many real-world complex networks have scale free (SF), small world (SW) properties, therefore, identification of influential spreaders in general artificial SF, SW as well as random networks will be more appealing. This research finds that, for artificial ER and SW networks, degree is more reliable than k-shell in predicting the outcome of spreading. However, for artificial SF networks, k-shell is remarkably reliable than degree and betweeness, which indicate that the four recently investigated real-world networks [Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E, Makse H A, Identification of influential spreaders in complex networks, Nat. Phys., 2010, 6: 888–893.] are more similar to scale free ones. Moreover, the investigations also indicate us an optimal dissemination strategy in networks with scale free property. That is, starting from moderate-degree-nodes will be ok and even more economical, since one can derive roughly similar outcome with starting from hubs.
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