Upload
milton-morton
View
212
Download
0
Embed Size (px)
Citation preview
Synopsis of “Emergence
of Scaling in
Random Networks”*
*Albert-Laszlo Barabasi and Reka Albert, Science, Vol 286, 15 October 1999
Presentation for ENGS 112
Doug Madory
Wed, 27 APR 05
Background
Traditional approach - random graph theory of Erdos and RenyiRarely tested in real world
Current technology allows analysis of large complex networks (Ex: WWW, citation patterns in science, etc)
Barabasi’s Claim
Independent of system and identity of its constituents, the probability P(k) that a vertex in the network interacts with k other vertices decays as a power law, following:
P(k) ~ k-
Existing network models fail to incorporate growth and preferential attachment, two key features of real networks.
Complex network examplesActor collaboration WWW Power grid data
Citations in published papers: cite = 3
Problems with other theories
Erdos-Renyi & Watts-Strogatz theories suggest probability of finding a highly connected vertex (large k) decreases exponentially with k Vertices with large k are practically absent
Barabasi - power-law tail characterizing P(k) for networks studied indicates that highly connected (large k) vertices have a large chance of occurring and dominating the connectivity
Problems with other theories
Erdos-Renyi & Watts-Strogatz assume fixed number (N) of vertices
Barabasi - real world networks form by continuous addition of new vertices, thus N increases throughout lifetime of network.
Problems with other theories
Erdos-Renyi & Watts-Strogatz - probability that two vertices are connected is random and uniform
Barabasi - real networks exhibit preferential connectivity New actor cast supporting established one New webpage will link to established pages
Barabasi’s Experiment Start with small number of vertices: mO At each time step, add new vertex with m(<=mO)
edges that link new vertex to m previous vertices Probability that a new vertex will be connected
to vertex i depends on connectivity ki of that vertex
ki) = ki/jkj (Preferential attachment)
Demo in Matlab
The “rich get richer” theory Similar mechanisms could explain the origin of social
and economic disparities governing competitive systems, because scale-free inhomogeneities are the inevitable consequence of self-organization due to local decisions made by individual vertices, based on information that is biased toward more visible (richer) vertices, irrespective of the nature and origin of this visibility.
Summary
Common property of many large networks is vertex connectivities follow a scale-free power-law distribution.
Consequence of two generic mechanisms:
(i) networks expand continuously by the addition of new vertices, and
(ii) new vertices attach preferentially to sites that are already well connected.