Social Networks: Advertising, Pricing and All That

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

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Social Networks (Continued)
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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

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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

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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

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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

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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

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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.
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Motivation

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

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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

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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

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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

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Bootstrapping Details

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

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Model (continued)

• The system updates the social network
• If a recommended agent buys the product, then the
  recommenders trustworthiness is increased by
  and the recommender is paid by the seller.
• If a recommended agent decides not to buy the
  product, then the recommenders trustworthiness
  is decreased by
• Two not previously connected agents who both buy
  the product, have probability to be connected
  in the next time step.

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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