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Social Networks: Advertising, Pricing and All That

Social Networks: Advertising, Pricing and All That. Zvi Topol & Itai Yarom. Agenda. Introduction Social Networks E-Markets Motivation Cellular market Web-services Model Discussion. Social Networks. Set of people or groups that are interconnected in some way Examples: Friends

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Social Networks: Advertising, Pricing and All That

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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

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