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  • 1. Social Networks: Advertising, Pricing and All That Zvi Topol & Itai Yarom
  • 2. Agenda
    • Introduction
      • Social Networks
      • E-Markets
    • Motivation
      • Cellular market
      • Web-services
    • Model
    • Discussion
  • 3. Social Networks
    • Set of people or groups that are interconnected in some way
    • Examples:
      • Friends
      • Business contacts
      • Co-authors of academic papers
      • Intermarriage connections
      • Protagonists in plays and comics
  • 4. Social Networks (Continued)
  • 5. Social Networks - Applications
    • Information diffusion in social networks
    • Epidemic spreading within different populations
    • Virus spreading among infected computers
    • WWW structure
    • Linguistic and cultural evolution
    • Dating, Jobs, Class reunions
  • 6. Social Network (continued)
    • Popular books:
  • 7. Properties of Networks
    • Diameter of the network:
      • Average geodesic distance
      • Maximal geodesic distance
    • Degree distributions
      • Regular graphs
      • Binomial/Poisson
      • Exponential
    • Clustering/Transitivity/Network Density
      • If vertex A is connected to vertex B and vertex B is connected to vertex C, higher prob. that vertex A is connected to vertex C
      • Presence of triangles in the graph
      • Clustering coefficient :
  • 8. Properties of Networks (continued)
    • Degree correlations – preferential attachment of high degree vertices/low degree vertices
    • Network resilience/tolerance – effects on the network when nodes are removed in terms of
      • Connectivity and # of components
      • # of paths
      • Flow
  • 9. Small World Models
    • Milgram conducted in the 60s a controversial experiment whose “conclusion” was 6 degrees of separation – “small world effect”
    • In their study Watts and Strogatz validated the effect on datasets and showed that real world networks are a combination of random graphs and regular lattices (low dimensional lattices with some randomness)
    • Barabasi et al showed that the degree distribution of many networks is exponential
  • 10. E-Markets
    • E-commerce opens up the opportunity to trade with information, e.g., single articles, customized news, music, video
    • E-marketplaces enable users to buy/sell information commodities
    • Information intermediaries can enrich the interactions and transactions implemented in such markets
  • 11. E-Markets Examples
    • Stock market (Continuous Double Auction)
      • Agents can outperform humans in unmixed markets and have similar performance in mixed markets (of humans and agents) [1]
    • Price posting markets
      • Cyclic price wars behavior occurs [2]
    • What are the roles that agents can take in those markets?
      • Agent can handle large amount of information and never get tired
    [1] Agent-Human Interactions in the Continuous Double Auction, Das, Hanson, Kephart and Tesauro, IJCAI-01. [2] The Role of Middle-Agents in Electronic Commerce, Itai Yarom, Claudia V. Goldman, and Jeffrey S. Rosenschein. IEEE Intelligent System special issue on Agents and Markets, Nov/Dec 2003, pp. 15-21.
  • 12. Motivation
    • Ubiquitous markets scenarios:
      • Cellular phones
      • Web services
    • Applications:
      • Sale on demand
      • Advertising
  • 13. Model
    • Social Network where:
      • A is set of rational economic agents
      • E is set of edges connecting agents, representing (close) social connections
    • SN is weighted according to the function
      • Where T is a trust domain, usually T = [0, 1]
      • We look at trust as a partial binary relation, i.e.
      • Let , then an edge e connecting both agents is in E iff
  • 14. Model (continued)
    • A seller s would like to use the Social Network to sell his product and bears a marginal cost function for production of
    • We look at a repeated game, at the beginning of which he approaches a set of recommenders from SN and acts according to the following protocol:
  • 15. Model (continued)
      • Seller: approaches potential recommenders
      • Recommender: sends list of recommended friends to seller
      • Seller: receives list of recommended customers (friends) and pays according to the function
      • Seller: approaches list of recommended friends
      • Customer (friend): decides whether to purchase the product
      • Recommenders: further remunerated according to
      • Seller: updates internal model of social network structure
  • 16. Bootstrapping Details
    • An initial scale-free network
    • No prior knowledge of seller about the structure of the network
    • Initial recommenders are picked randomly
  • 17. Model (continued)
    • The system updates the social network:
      • If a recommended agent buys the product, then the recommender’s trustworthiness is increased by and the recommender is paid by the seller.
      • If a recommended agent decides not to buy the product, then the recommender’s trustworthiness is decreased by
      • Two not previously connected agents who both buy the product, have probability to be connected in the next time step.
  • 18. Discussion
    • Buyers want to identify the money maker recommenders
    • Friend of a friend recommendation (different depths along the chain)
    • Learning of Social Network behavior
    • Relevant research