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