lecture6 notes

Empirically studied by salganik dodds and watts 2006

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: quite fragile, there is great sensitivity to unpredictable initial fluctuations. Empirically studied by Salganik, Dodds and Watts (2006): They created a music download site with 48 obscure songs. A visitor to the site can listen to the songs and also is shown the “current” download count for each song. Each visitor at random is assigned to 8 “parallel copies” of the site, which started out identically. Market share of different songs varied considerably across different copies. 9 Networks: Lecture 6 History of Power Laws—3 In 1965, Price applied these ideas to networks, with a particular focus on citation networks. Price studied the network of citations between scientific papers and found that the in degrees (number of times a paper has been cited) have power law distributions. His idea was that an article would gain citations over time in a manner proportional to the number of citations the paper already had. This is consistent with the idea that researchers find some article (e.g. via searching for keywords on the Internet) and then search for additional papers by tracing through the references of the first article. The more citations an article has, the higher the likelihood that it will be found and cited again. Price called this dynamic link formation process cumulative advantage. Today it is known under the name preferential attachment after the influential work of Barabasi and Albert in 1999. 10 Networks: Lecture 6 Uniform Attachment Model Before studying the preferential attachment model, we discuss a dynamic variation on the Erd¨s-Renyi model, in which nodes are born over time and o form edges to existing nodes at the time of their birth. Index the nodes by the order of their birth, i.e., node i is born at date i , i = 0, 1, . . .. A node forms undirected edges to existing nodes when it is born. Let di (t ) be the degree of node i at time t . Start the network with m + 1 nodes (born at times 0, . . . , m) all connected to one another. Thus, the first newborn node is the one born at time m + 1. Assume that each newborn node uniformly randomly selects m nodes from the existing set of nodes and links to them (ignore repetitions). 11 Networks: Lecture 6 Evolution of Expected Degrees We will use a continuous-time mean-field analysis to track the evolution of the “expected degrees of nodes”. We have the initial condition di (i ) = m for all i , every node has m links at their birth. The change at time t > i of the expected degree of node i is given by...
View Full Document

This document was uploaded on 03/18/2014 for the course EECS 6.207J at MIT.

Ask a homework question - tutors are online