0e36542f69dd428697ce95c8d9cc676a.ppt

- Количество слайдов: 18

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 – Business contacts – Co-authors of academic papers – Intermarriage connections – Protagonists in plays and comics –…

Social Networks (Continued)

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 • …

Social Network (continued) • Popular books:

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 :

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 –… • …

Small World Models • Milgram conducted in the 60 s 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

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

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.

Motivation • Ubiquitous markets scenarios: – Cellular phones – Web services • Applications: – Sale on demand – Advertising

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

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:

Model (continued) 1. 2. 3. 4. 5. 6. 7. 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

Bootstrapping Details • An initial scale-free network • No prior knowledge of seller about the structure of the network • Initial recommenders are picked randomly

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.

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