INNOVATION SPREADING: A PROCESS ON MULTIPLE SCALES Jnos Kertsz
Central European University Center for Network Science Lorentz
Centre, Leiden, 2013
Slide 2
In collaboration with: Mrton Karsai Northeastern University
Universit de Lyon Gerardo Iiguez Kimmo Kaski Aalto University Ando
Sabbas Skype Research Labs Marlon Dumas Tartu University
Slide 3
Outline -Role of innovations in economy -Innovation diffusion
-Skype data and network characteristics -Mean field theory of
spreading -Predictions, scenarios and correlations with global
characteristics -Summary, to do
Slide 4
Role of innovation in economy Equilibrium theories: Static
view. There are needs (demand), which can be satisfied by supply of
goods and services at the price determined by their balance. Change
one parameter and assume smooth dependence. Economic growth:
Non-equilibrium. Increasing productivity, new products, new demand.
(Schumpeters creative destruction). Key element: Innovation
Innovation: creation of novel values through invention, ideas,
technologies, processes.
Slide 5
In 1898 the first international urban planning conference
convened in New York. One topic dominated discussion: manure.
Cities all over the world, including Sydney, were experiencing the
same problem. Unable to see any solution to the manure crisis, the
delegates abandoned the conference after three days instead of the
scheduled ten days. Then, quite quickly, the crisis passed as
millions of horses were replaced by millions of motor vehicles.
Cars were cheaper to own and operate than horse-drawn vehicles,
both for the individual and for society. In 1900, 4,192 cars were
sold in the US; by 1912 that number had risen to 356,000. In 1912,
traffic counts in New York showed more cars than horses for the
first time.
http://bytesdaily.blogspot.nl/2011/07/great-horse-manure-crisis-of-1894.html
Slide 6
Invention is not enough, success is needed! (see, e.g., typing
keyboard as a counterexample) Spreading (diffusion) of innovations
For success the innovation has to spread through the target
population. Verbal theory (E.M. Rogers) Innovators: 2.5% Early
Adopters: 13.5% Early majority: 34% Late majority 34% Laggards
16%
Slide 7
Spreading mechanism Network effects are crucial Mahajan, Muller
and Bass (1990 ) Adoption ratep: probability of adoption m: market
potential
Slide 8
Diffusion networks -Two effects: peer communication and mass
media -Social learning theory (microscopic mechanism) -Sociological
aspects (Opinion leadership, homophily as a barrier) -Analogies and
differences to epidemic spreading -SOCIAL NETWORK STRUCTURE,
cascading on networks
Slide 9
Mathematical models for (epidemic) spreading Nodes can be in
different states Susceptible (S) Target population for innovation:
not yet adopters Infected (I) Adopters Recovered (R) Terminated
Different rates describe the transitions between these states,
depending on the microscopic details of the process. In epidemics,
if I meets S, S I, I R spontaneously, R S sometimes etc.
Accordingly, there are families of spreading models: SI SIR SIRS
etc. Huge amount of literature (e.g. Barrat, Barthelmy, Vespignani
book)
Slide 10
Effect of the network structure on spreading Network of social
contacts has nontrivial mesoscale structure: There are strongly
wired communities con- nected by weak ties The strength of weak
ties Granovetter 1976 Onnela et al. PNAS, 2007
Slide 11
Diffusion of information Knowledge of information diffusion
based on unweighted networks Use the empirical network to study
diffusion on a weighted network: Does the local relationship
between topology and tie strength have an effect? Spreading
simulation: infect one node with new information (1) Empirical: p
ij w ij (2) Reference: p ij Spreading significantly faster on the
reference (average weight) network Information gets trapped in
communities in the real network SI dynamics Reference
Empirical
Slide 12
Diffusion of information Where do individuals get their
information? Majority of both weak and strong ties have subordinate
role as information sources! ReferenceEmpirical The importance of
intermediate ties!
Slide 13
Correlations influence spreading -Topology (community
structure) -Weight-topology -Daily pattern -Bursty dynamics
-Link-link dynamic correlations Karsai et al. PRE (R) 2011
Slide 14
Correlations influence spreading Event stamps based simulation
Reference systems by appropriate shuffling. Dominant decelerating
effect Weight-topology + burstiness
Slide 15
Innovation spreading in the society Data from Skype:
Information about: -Basic service network -Adoption of additional
services -Data about location (IP)
Slide 16
Social network layer
Slide 17
Online social network layer
Slide 18
Online service network layer unknown
Slide 19
Separation of time scales
Slide 20
Skype slides missing
Slide 21
Summary Innovations are crucial for understanding the dynamics
of the economy Diffusion of innovation is a mechanism with
parallels and differences to spreading of diseases Network
correlations influence spreading speed significantly Skype data are
ideal to study diffusion of innovation, which can be modeled as
adoption and terminating process Basic processes are: Spontaneous
adoption, peer pressure, temporal halt and terminating We verified
that pear pressure is proportional to the rate of adopting
neighbors Mean field works surprisingly well Correlations with
country characteristics
Slide 22
Thank you! NOTE: Postdoc position open at CEU Center for
Network Science Contact me: [email protected]
