Identifying influential spreaders by gravity model

• Published in: Scientific reports Vol. 9; no. 1; p. 8387
• Main Authors: Li, Zhe, Ren, Tao, Ma, Xiaoqi, Liu, Simiao, Zhang, Yixin, Zhou, Tao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.06.2019; Nature Publishing Group
• Subjects: 639/766/530/2801; 639/766/530/2804; Algorithms; Collaboration; Computer applications; Epidemics; Gravity; Humanities and Social Sciences; Influence; Information processing; multidisciplinary; Neighborhoods; Science; Science (multidisciplinary); Social networks; Spreading

Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and pa...