1 / 34

News from Web Science 2012

News from Web Science 2012. New models for power law : Hans Akkermans : using continuous time Many models may lead to power laws Differential equations October 18: Talk by Hans Akkermans Hot topic : Distinguishing influence from co-occurrence Fowler

Download Presentation

News from Web Science 2012

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. News from Web Science 2012 • New modelsfor power law: • Hans Akkermans: usingcontinuous time • Manymodelsmayleadto power laws • Differential equations • October 18: Talk by Hans Akkermans • Hot topic: Distinguishinginfluencefromco-occurrence • Fowler • Political influence – doingexperimentsmotivatingpeopletovote • Sinan Aral • New probabilisticmodels • Tests with viral marketing on the Web

  2. News from Web Science 2012 • Strengthofweakties: • Nodes with high in-betweennessaresaidtoexcel • In practicetheyoften do not • New researchformalizesmorepreciselywhenthey do excel (e.g. earnmoremoneyratherthangettingnervousbreakdowns)

  3. Network Theory and Dynamic SystemsCascading Behavior in Networks Prof. Dr. Steffen Staab Dr. Christoph Ringelstein

  4. Howcould • thefirsttelephone/ • firsthundredtelephones everbesold?

  5. Diffusion in Networks • Information cascade, Network effects, and Rich-get-richer: • View the network as a relatively amorphous population of individuals, and look at effects in aggregate; • Global level • Diffusion in networks: • View the fine structure of the network as a graph, and look at how individuals are influenced by their particular network neighbors. • Local level

  6. Diffusion of Innovations • Considering how new • behaviors, • practices, • opinions, • conventions, and • technologies spread from person to person through a social network • Success depends on: • Relative advantage • Complexity • Observability • Trialability • Compatibility

  7. A Networked Coordination Game • If nodes v and w are linked by an edge, then there is an incentive for them to have their behaviors match • v and w are the players • Two possible behaviors, labeled A and B • A and B are the possible strategies • The payoffs are defined as follows: • if v and w both adopt behavior A, they each get a payoff of a > 0; • if they both adopt B, they each get a payoff of b > 0; and • if they adopt opposite behaviors, they each get a payoff of 0.

  8. A Networked Coordination Game • Suppose that some of v neighbors adopt A, and some adopt B; what should v do in order to maximize its payoff? • v has d neighbors • a p fraction of them have behavior A • a1-p fraction have behavior B • If v chooses A, it gets a payoff of pda • If v chooses B, it gets a payoff of (1-p)da

  9. A Networked Coordination Game • The game is played along all edges

  10. A Networked Coordination Game • A is a better choice if: Or: • Threshold rule: If at least a q = b/(a+b) fraction of your neighbors follow behavior A, then you should too.

  11. Cascading Behavior • Two obvious equilibria: • Everyone adopts A, and • Everyone adopts B. • How easy is it to ‘tip’ a network? • Initially everyone is using B • A few early adopters are using A

  12. Tip a = 3 b = 2 q = 2/(3+2) = 2/5

  13. Intermediate Equilibria a = 3 b = 2 q = 2/(3+2) = 2/5

  14. Intermediate Equilibria

  15. Intermediate Equilibria tightly-knit communities in the network can work to hinder the spread of an innovation

  16. Cascades of adoption • The chain reaction of switches to Ais called a cascade of adoptions of A, • Two fundamental possibilities exist: (i) that the cascade runs for a while but stops while there are still nodes using B, or (ii) that there is a complete cascade, in which every node in the network switches to A. • If the threshold to switch is q, we say that the set of initial adopters causes a complete cascade at threshold q.

  17. Viral Marketing • Strategy I: Making an existing innovation slightly more attractive can greatly increase its reach. • E.g. in our example: Changing a = 3 to a = 4 (new threshold q = 1/3) will results in a complete cascade.

  18. Viral Marketing • Strategy II: convince a small number of key people in the part of the network using B to switch to A. • E.g. in our example: Convincing node 12 or 13 to switch to A will cause all of nodes 11–17 to switch. (Convincing 11 or 14 would not work)

  19. Reflection • Population-level network effects • Decisions based on the overall fraction. • Hard to start for a new technology, even when it is better. • Network-level cascading adoption • Decisions based on immediate neighbors • A small set of initial adopters are able to start a cascade.

  20. Cascades and Clusters • Definition (Densely connected community): We say that a cluster of density p is a set of nodes such that each node in the set has at least a p fraction of its network neighbors in the set. • the set of all nodes is always a cluster of density 1 • the union of two clusters with density p has also density p • Homophily can often serve as a barrier to diffusion, by making it hard for innovations to arrive from outside densely connected communities. Three four node clusters with density d = 2/3

  21. Relationship between Clusters and Cascades Claim: Consider a set of initial adopters of behavior A, with a threshold of q for nodes in the remaining network to adopt behavior A. (i) If the remaining network contains a cluster of density greater than 1 − q, then the set of initial adopters will not cause a complete cascade. (ii) Moreover, whenever a set of initial adopters does not cause a complete cascade with threshold q, the remaining network must contain a cluster of density greater than 1 − q.

  22. Example Clusters of density greater than 1 − 2/5 = 3/5 block the spread of A at threshold 2/5 .

  23. Part (i): Clusters are Obstacles to Cascades The spread of a new behavior, when nodes have threshold q, stops when it reaches a cluster of density greater than (1 − q).

  24. Part (ii): Clusters are the Only Obstacles to Cascades If the spread of A stops before filling out the whole network, the set of nodes that remain with B form a cluster of density greater than 1 − q.

  25. Diffusion, Thresholds, and the Role of Weak Ties There is a crucial difference between learning about a new idea and actually deciding to adopt it. The years of first awareness and first adoption for hybrid seed corn in the Ryan-Gross study.

  26. Example Steps 11-14 become aware of A but never adopt it.

  27. Strength-of-weak-ties The u-w and v-w edges are more likely to act as conduits for information than for high-threshold innovations. Initial adopters: w and x Threshold: q = 1/2

  28. Extensions of the Basic Cascade Model Heterogeneous Thresholds: • Suppose that each person in the social network values behaviors A and B differently => each node v, has its own payoffs avand bv • Now, A is the better choice if • Each node v has its own personal threshold qv, and it chooses A if at least a qvfraction of its neighbors have done so. = qv

  29. Example & Influenceable nodes Influenceable nodes are nodes with a low threshold

  30. Example & Blocking cluster Blocking cluster in the network is a set of nodes for which each node v has more than a 1−qvfraction of its friends also in the set. A set of initial adopters will cause a complete cascade (with a given set of node thresholds) if and only if the remaining network does not contain a blocking cluster.

  31. Knowledge, Thresholds, and Collective Action Integrating network effects at both the population level and the local network level. • We consider situations where coordination across a large segment of the population is important, and the underlying social network is serving to transmit information about people’s willingness to participate. • Collective action problem: A positive payoff if a lot of people participate, a negative payoff if only a few participate (e.g. protest under a repressive regime). • Pluralistic ignorance: People have wildly erroneous estimates about the prevalence of certain opinions in the population at large.

  32. A Model for the Effect of Knowledge on Collective Action • Suppose that each person in a social network has a personal threshold which encodes her willingness to participate. • A threshold of k means, “I will show up for the protest if I am sure that at least k people in total (including myself) will show up.” • Each node only knows its and its neighbors threshold

  33. Common Knowledge and Social Institutions. • A widely-publicized speech, or an article in a high-circulation newspaper, has the effect not just of transmitting a message, but of making the listeners or readers realize that many others have gotten the message as well

  34. Andthefirsttelephonescouldbesold, becausetheycouldprovidesufficientbenefit in a tightlyenoughknitnetwork! • Not due topopulationcounts!

More Related