I’ve had “Networks, Crowds, and Markets: Reasoning About a Highly Connected World” by David Easley and Jon Kleinberg on my bookshelf for a few months now, and a conversation with a client reminded me that I hadn’t finished reading it (barely started really). It is available from Cambridge University Press, but also on the web and in PDF format.
If you’d like to get a feel for the kinds of talks Jon Kleinberg gives, check out some these videos:
ITA 2010 – Cascading Behavior in Complex Networks, Jon Kleinberg
In many domains, the spread of information through a network can trigger forms of cascading behavior. We focus particularly on large social networks, where the flow of information or influence can be thought of as unfolding with the dynamics of an epidemic: as individuals become aware of new ideas, technologies, fads, rumors, or gossip, they have the potential to pass them on to their friends and colleagues, causing the resulting behavior to cascade through the network. We consider a collection of models for such phenomena proposed in the mathematical social sciences, as well as recent algorithmic work on the problem by computer scientists. Building on this, we discuss the implications of cascading behavior in a number of settings, relating the basic models to recent empirical studies of large-scale on-line network data.
What can Facebook, Amazon and Google teach us about society and about ourselves?
With an increasing amount of social interaction taking place in the digital domain, often in public settings, we are accumulating enormous amounts of data about phenomena that were once essentially invisible to us: the collective behavior and social interactions of hundreds of millions of people, recorded at unprecedented levels of scale and resolution.
Perspectives on Social Phenomena in Online Networks
Jon Kleinberg focuses his discussion on issues that come up when thinking about social phenomenon on the web and some how it interacts with some of the classical theories from the social sciences.
… and if that doesn’t convince you to get the book, just take a look at this impressive table of contents:
Chapter 1. Overview
1.1 Aspects of Networks
1.2 Central Themes and Topics
Part I Graph Theory and Social Networks
Chapter 2. Graphs
2.1 Basic Definitions
2.2 Paths and Connectivity
2.3 Distance and Breadth-First Search
2.4 Network Datasets: An Overview
Chapter 3. Strong and Weak Ties
3.1 Triadic Closure
3.2 The Strength of Weak Ties
3.3 Tie Strength and Network Structure in Large-Scale Data
3.4 Tie Strength, Social Media, and Passive Engagement
3.5 Closure, Structural Holes, and Social Capital
3.6 Advanced Material: Betweenness Measures and Graph Partitioning
Chapter 4. Networks in Their Surrounding Contexts
4.1 Homophily
4.2 Mechanisms Underlying Homophily: Selection and Social Influence
4.3 Affiliation
4.4 Tracking Link Formation in On-Line Data
4.5 A Spatial Model of Segregation
Chapter 5. Positive and Negative Relationships
5.1 Structural Balance
5.2 Characterizing the Structure of Balanced Networks
5.3 Applications of Structural Balance
5.4 A Weaker Form of Structural Balance
5.5 Advanced Material: Generalizing the Definition of Structural Balance
Part II Game Theory
Chapter 6. Games
6.1 What is a Game?
6.2 Reasoning about Behavior in a Game
6.3 Best Responses and Dominant Strategies
6.4 Nash Equilibrium
6.5 Multiple Equilibria: Coordination Games
6.6 Multiple Equilibria: The Hawk-Dove Game
6.7 Mixed Strategies
6.8 Mixed Strategies: Examples and Empirical Analysis
6.9 Pareto-Optimality and Social Optimality
6.10 Advanced Material: Dominated Strategies and Dynamic Games
Chapter 7. Evolutionary Game Theory
7.1 Fitness as a Result of Interaction
7.2 Evolutionarily Stable Strategies
7.3 A General Description of Evolutionarily Stable Strategies
7.4 Relationship Between Evolutionary and Nash Equilibria
7.5 Evolutionarily Stable Mixed Strategies
Chapter 8. Modeling Network Traffic using Game Theory
8.1 Traffic at Equilibrium
8.2 Braess’s Paradox
8.3 Advanced Material: The Social Cost of Traffic at Equilibrium
Chapter 9. Auctions
9.1 Types of Auctions
9.2 When are Auctions Appropriate?
9.3 Relationships between Different Auction Formats
9.4 Second-Price Auctions
9.5 First-Price Auctions and Other Formats
9.6 Common Values and The Winner’s Curse
9.7 Advanced Material: Bidding Strategies in First-Price and All-Pay Auctions
Part III Markets and Strategic Interaction in Networks
Chapter 10. Matching Markets
10.1 Bipartite Graphs and Perfect Matchings
10.2 Valuations and Optimal Assignments
10.3 Prices and the Market-Clearing Property
10.4 Constructing a Set of Market-Clearing Prices
10.5 How Does this Relate to Single-Item Auctions?
10.6 Advanced Material: A Proof of the Matching Theorem
Chapter 11. Network Models of Markets with Intermediaries
11.1 Price-Setting in Markets
11.2 A Model of Trade on Networks
11.3 Equilibria in Trading Networks
11.4 Further Equilibrium Phenomena: Auctions and Ripple Effects
11.5 Social Welfare in Trading Networks
11.6 Trader Profits
11.7 Reflections on Trade with Intermediaries
Chapter 12. Bargaining and Power in Networks
12.1 Power in Social Networks
12.2 Experimental Studies of Power and Exchange
12.3 Results of Network Exchange Experiments
12.4 A Connection to Buyer-Seller Networks
12.5 Modeling Two-Person Interaction: The Nash Bargaining Solution
12.6 Modeling Two-Person Interaction: The Ultimatum Game
12.7 Modeling Network Exchange: Stable Outcomes
12.8 Modeling Network Exchange: Balanced Outcomes
12.9 Advanced Material: A Game-Theoretic Approach to Bargaining
Part IV Information Networks and the World Wide Web
Chapter 13. The Structure of the Web
13.1 The World Wide Web
13.2 Information Networks, Hypertext, and Associative Memory
13.3 The Web as a Directed Graph
13.4 The Bow-Tie Structure of the Web
13.5 The Emergence of Web 2.0
Chapter 14. Link Analysis and Web Search
14.1 Searching the Web: The Problem of Ranking
14.2 Link Analysis using Hubs and Authorities
14.3 PageRank
14.4 Applying Link Analysis in Modern Web Search
14.5 Applications beyond the Web
14.6 Advanced Material: Spectral Analysis, Random Walks, and Web Search
Chapter 15. Sponsored Search Markets
15.1 Advertising Tied to Search Behavior
15.2 Advertising as a Matching Market
15.3 Encouraging Truthful Bidding in Matching Markets: The VCG Principle
15.4 Analyzing the VCG Procedure: Truth-Telling as a Dominant Strategy
15.5 The Generalized Second Price Auction
15.6 Equilibria of the Generalized Second Price Auction
15.7 Ad Quality
15.8 Complex Queries and Interactions Among Keywords
15.9 Advanced Material: VCG Prices and the Market-Clearing Property
Part V Network Dynamics: Population Models
Chapter 16. Information Cascades
16.1 Following the Crowd
16.2 A Simple Herding Experiment
16.3 Bayes’ Rule: A Model of Decision-Making Under Uncertainty
16.4 Bayes’ Rule in the Herding Experiment
16.5 A Simple, General Cascade Model
16.6 Sequential Decision-Making and Cascades
16.7 Lessons from Cascades
Chapter 17. Network Effects
17.1 The Economy Without Network Effects
17.2 The Economy with Network Effects
17.3 Stability, Instability, and Tipping Points
17.4 A Dynamic View of the Market
17.5 Industries with Network Goods
17.6 Mixing Individual Effects with Population-Level Effects
17.7 Advanced Material: Negative Externalities and The El Farol Bar Problem
Chapter 18. Power Laws and Rich-Get-Richer Phenomena
18.1 Popularity as a Network Phenomenon
18.2 Power Laws
18.3 Rich-Get-Richer Models
18.4 The Unpredictability of Rich-Get-Richer Effects
18.5 The Long Tail
18.6 The Effect of Search Tools and Recommendation Systems
18.7 Advanced Material: Analysis of Rich-Get-Richer Processes
Part VI Network Dynamics: Structural Models
Chapter 19. Cascading Behavior in Networks
19.1 Diffusion in Networks
19.2 Modeling Diffusion through a Network
19.3 Cascades and Clusters
19.4 Diffusion, Thresholds, and the Role of Weak Ties
19.5 Extensions of the Basic Cascade Model
19.6 Knowledge, Thresholds, and Collective Action
19.7 Advanced Material: The Cascade Capacity
Chapter 20. The Small-World Phenomenon
20.1 Six Degrees of Separation
20.2 Structure and Randomness
20.3 Decentralized Search
20.4 Empirical Analysis and Generalized Models
20.5 Core-Periphery Structures and Difficulties in Decentralized Search
20.6 Advanced Material: Analysis of Decentralized Search
Chapter 21. Epidemics
21.1 Diseases and the Networks that Transmit Them
21.2 Branching Processes
21.3 The SIR Epidemic Model
21.4 The SIS Epidemic Model
21.5 Synchronization
21.6 Transient Contacts and the Dangers of Concurrency
21.7 Genealogy, Genetic Inheritance, and Mitochondrial Eve
21.8 Advanced Material: Analysis of Branching and Coalescent Processes
Part VII Institutions and Aggregate Behavior
Chapter 22. Markets and Information
22.1 Markets with Exogenous Events
22.2 Horse Races, Betting, and Beliefs
22.3 Aggregate Beliefs and the “Wisdom of Crowds”
22.4 Prediction Markets and Stock Markets
22.5 Markets with Endogenous Events
22.6 The Market for Lemons
22.7 Asymmetric Information in Other Markets
22.8 Signaling Quality
22.9 Quality Uncertainty On-Line: Reputation Systems and Other Mechanisms
22.10 Advanced Material: Wealth Dynamics in Markets
Chapter 23. Voting
23.1 Voting for Group Decision-Making
23.2 Individual Preferences
23.3 Voting Systems: Majority Rule
23.4 Voting Systems: Positional Voting
23.5 Arrow’s Impossibility Theorem
23.6 Single-Peaked Preferences and the Median Voter Theorem
23.7 Voting as a Form of Information Aggregation
23.8 Insincere Voting for Information Aggregation
23.9 Jury Decisions and the Unanimity Rule
23.10 Sequential Voting and the Relation to Information Cascades
23.11 Advanced Material: A Proof of Arrow’s Impossibility Theorem
Chapter 24. Property Rights
24.1 Externalities and the Coase Theorem
24.2 The Tragedy of the Commons
24.3 Intellectual Property
Filed under: Problems, Random Tagged: centrality, cluster, graph, graph database, max flow, network, pagerank, six degrees of kevin bacon
