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) 1.Seller: approaches potential recommenders 2.Recommender: sends list of recommended friends to seller 3.Seller: receives list of recommended customers (friends) and pays according to the function 4.Seller: approaches list of recommended friends 5.Customer (friend): decides whether to purchase the product 6.Recommenders: further remunerated according to 7.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