caa a22 4500 605514682 CHVBK 20210128100705.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s10458-015-9305-5 doi (NATIONALLICENCE)springer-10.1007/s10458-015-9305-5 Cross-layers cascade in multiplex networks [Elektronische Daten] [Zhaofeng Li, Fuhan Yan, Yichuan Jiang] The study of information cascade in multiplex networks, where agents are connected by using multiple linking types, has received increasing attention. Compared with the cascade in simplex networks, a noticeable characteristic of the cascade in multiplex networks is that information may be spread between multiple layers. In this study, we focus on the cross-layers cascade, which helps clarify two opposing opinions about the information cascade in multiplex networks: multiplexity can speed up or slow down information cascade. The linear threshold model is generalized into multiplex networks as conjoint agents become active, if the influences of active neighbors in any layer reach a predefined threshold. The preconditions and reasons for the slow-down and speed-up phenomena are discussed using four representative case studies and theoretical analyses. Next, analytical results are validated by using extensive simulations in which the multiplex networks are generated by random, small-world and scale-free network models. It is found that the slow-down phenomenon emerges due to the obstruction of cross-layers cascade which connects the distributed shortest path in multiple layers and the inhibitory effect of negative influence. Conversely, extra short paths or rapid spreading in one additional layer can facilitate the cascade process in existing networks, respectively. Extensive simulations also show that multiplex networks consisting of different network models are more competent for the cascade process compared with multiplex networks generated by a single network model. In conclusion, the concept of cross-layers cascade may elucidate the additional study of information spreading in multiplex networks. The Author(s), 2015 Information cascade nationallicence Multiplex networks nationallicence Cross-layers nationallicence Linear threshold model nationallicence Multi-agent systems nationallicence Li Zhaofeng College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China aut Yan Fuhan College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China aut Jiang Yichuan College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China aut Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/6(2015-11-01), 1186-1215 1387-2532 29:6<1186 2015 29 10458 https://doi.org/10.1007/s10458-015-9305-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10458-015-9305-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Zhaofeng College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China aut NATIONALLICENCE 700 1- Yan Fuhan College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China aut NATIONALLICENCE 700 1- Jiang Yichuan College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China aut NATIONALLICENCE 773 0- Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/6(2015-11-01), 1186-1215 1387-2532 29:6<1186 2015 29 10458