How Complex Contagions Spread Quickly in the Preferential Attachment Model and Other Time-Evolving Networks
چکیده
In this paper, we study the spreading speed of complex contagions in a social network. A k-complex contagion starts from a set of initially infected seeds such that any node with at least k infected neighbors gets infected. Simple contagions, i.e., k = 1, quickly spread to the entire network in small world graphs. However, fast spreading of complex contagions appears to be less likely and more delicate; the successful cases depend crucially on the network structure [19, 32]. Our main result shows that complex contagions can spread fast in a general family of time-evolving networks that includes the preferential attachment model [10]. We prove that if the initial seeds are chosen as the oldest nodes in a network of this family, a k-complex contagion covers the entire network of n nodes in O(log n) steps. We show that the choice of the initial seeds is crucial. If the initial seeds are uniformly randomly chosen in the PA model, even if we have a polynomial number of them, a complex contagion would stop prematurely. The oldest nodes in a preferential attachment model are likely to have high degrees. However, we remark that it is actually not the power law degree distribution per se that facilitates fast spreading of complex contagions, but rather the evolutionary graph structure of such models. Some members of the said family do not even have a power-law distribution. The main proof has two pillars. The first one is an analysis of a labeled branching process which might be of independent interest. The second pillar is an intricate coupling argument that links the extinction time of the labeled branching process to the speed of a k-complex contagion in the said family of time-evolving networks. The coupling argument itself relies on a careful revealing process that reveals the randomness of the network in a particular order to alleviate dependency/conditioning problems. Using similar techniques, we also prove that complex contagions are fast in the copy model [41], a variant of the preferential attachment family, if the initial seeds are chosen as the oldest nodes. Finally, we prove that when a complex contagion starts from an arbitrary set of initial seeds on a general graph, determining if the number of infected vertices is above a given threshold is P-complete. Thus, one cannot hope to categorize all the settings in which complex contagions percolate in a graph. keywords: Social Networks, Complex Contagion, The Preferential Attachment Model, The Copy Model, Time-Evolving Networks. Department of Computer Science, Stony Brook University, Stony Brook, NY 11794. {rebrahimi,jgao,gghasemiesfe}@cs.stonybrook.edu Department of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, MI 48109. schoeneb@umich.edu