JIGSAW PERCOLATION: WHAT SOCIAL NETWORKS CAN COLLABORATIVELY SOLVE A PUZZLE?

• 成果类型: Article
• 署名作者: Brummitt, Charles D.; Chatterjee, Shirshendu; Dey, Partha S.; Sivakoff, David
• 署名单位: University of California System; University of California Davis; New York University; University of Warwick; University System of Ohio; Ohio State University; University System of Ohio; Ohio State University
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/14-AAP1041
• 发表日期: 2015
• 页码: 2013-2038
• 关键词: long-range percolation; Random graphs

摘要

We introduce a new kind of percolation on finite graphs called jigsaw percolation. This model attempts to capture networks of people who innovate by merging ideas and who solve problems by piecing together solutions. Each person in a social network has a unique piece of a jigsaw puzzle. Acquainted people with compatible puzzle pieces merge their puzzle pieces. More generally, groups of people with merged puzzle pieces merge if the groups know one another and have a pair of compatible puzzle pieces. The social network solves the puzzle if it eventually merges all the puzzle pieces. For an Erdos Renyi social network with n vertices and edge probability p(n), we define the critical value Pc (n) for a connected puzzle graph to be the Pit for which the chance of solving the puzzle equals 1/2. We prove that for the n-cycle (ring) puzzle, p(c)(n) = Theta(1/log n), and for an arbitrary connected puzzle graph with bounded maximum degree, Pc (n) = Theta (1/log n) and omega(1/n(b)) for any b > 0. Surprisingly, with probability tending to 1 as the network size increases to infinity, social networks with a power-law degree distribution cannot solve any bounded-degree puzzle. This model suggests a mechanism for recent empirical claims that innovation increases with social density, and it might begin to show what social networks stifle creativity and what networks collectively innovate.