Section 8 – Ec1818 Jeremy Barofsky jbarofsk@hsph.harvard - PowerPoint PPT Presentation

Section 8 ec1818 jeremy barofsky jbarofsk@hsph harvard edu
1 / 16

  • Uploaded on
  • Presentation posted in: General

Section 8 – Ec1818 Jeremy Barofsky March 31 st and April 1 st , 2010. Section 8 Outline (lectures 15, 16). Social Network Introduction Types of Networks / Graphs Random Regular Small-world Erdos / Bacon Numbers Review Questions? Evaluations

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Download Presentation

Section 8 – Ec1818 Jeremy Barofsky jbarofsk@hsph.harvard

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Section 8 ec1818 jeremy barofsky jbarofsk@hsph harvard edu

Section 8 – Ec1818Jeremy

March 31st and April 1st, 2010

Section 8 outline lectures 15 16

Section 8 Outline (lectures 15, 16)

  • Social Network Introduction

  • Types of Networks / Graphs

    • Random

    • Regular

    • Small-world

  • Erdos / Bacon Numbers

  • Review Questions?

  • Evaluations

  • Office Hours - Thursday, 4/1/10 10-11am, outside 320 CGIS North.

Small world phenomenon milgram

Small World Phenomenon - Milgram

  • Question: Probability that two randomly selected people know each other?

  • Design: In 1967, Milgram sent packages to 160 random people living in Omaha NE asking them to send the package to a friend or acquaintance they thought might know or be connected to the final individual – a stock broker in Boston. (Postcards also sent back to Harvard to track progress).

  • Results:

    1) Of those letters that found destination, average path length 5.5-6.

    2) Significant selection bias – in one experiment 232 of 296 were not sent on.

    3) Most of cards given to target through a few people. Experiment with 160 packages sent, 24 reached target at his home and 16 of these were given to target by one person (nodes in network).

    -Reasons for under-estimate or over-estimate of avg. path length?

Social networks

Social networks

  • A graph G consists of a set V(G) of vertices (or nodes) together with a set of edges E(G) (or links) that connect vertices.

  • Degree: number of edges connected to a given vertex.

  • Order: the number of vertices V(G) in graph G represent its order.

  • Size: the number of edges E(G) in G represents its size.

  • Directed graph / undirected graph: graph is directed if all its edges are directional, ie- the network tells us not just whether people are friends but whether each person considers the other a friend. If none of edges are directional, then graph G is undirected.

Networks graphs and 3 elements

Networks / Graphs and 3 elements

Social networks metrics

Social Networks Metrics

  • Characteristic path length L(G, p): measures average distance between vertices. By distance we mean the shortest path that connects vertices v and v’.

  • Clustering coefficient C(G, p): Measures a vertex / person’s level of cliquishness within its neighborhood. Answers – are the friends of my friends, my friends also?

  • Formally C(G, p)= actual edges in network within its neighborhood / maximum possible edges in that neighborhood.

  • Maximum number of graph edges / number of connections in social network: n(n-1)/2 where n = number of vertices.

Types of graphs

Types of Graphs

  • Regular Network: each vertex is connected to same number k of their nearest neighbors only. All vertices have the same degree. Long characteristic path length because takes a long time to get from one vertex to another, large clustering coefficient because vertices connected to all other nearby vertices.

  • Random Network: Edges between vertices occur randomly with prob. = 1/V(G). Full connectedness occurs non-linearly when Pr(connection) = 1/V(G). Small characteristic path length and clustering coefficient.

  • Adjacency matrix: Way to represent network data with each row/ column representing whether those vertices have a connection.

From regular to random graphs via small worlds

From Regular to Random Graphs via Small Worlds

Regular small world random graphs

Regular -> Small World -> Random Graphs

  • Rewire: Start with a regular graph with vertices in a circle and each connected to 4 closest neighbors. Rewire each edge at random with probability p.

  • Changing p means tunes graph such that p = 0 defines a regular graph, p = 1 random.

  • Watts and Strogratz define small-world networks with two characteristics:

  • Large Clustering Coefficient C(G, p) – most of my neighbors are friends and friends with me too.

  • Small Characteristic Path Length L(G, p) – Presence of random, long-distance connections mean that moving from one part of the graph to the other can be done quickly.

Section 8 ec1818 jeremy barofsky jbarofsk hsph harvard

Characteristic path length L(p) and clustering coefficient C(p) for rewired graphs as p varies. (Watts and Strogatz, 1998)

Empirical examples watts and strogatz 1998

Empirical Examples (Watts and Strogatz, 1998)

Power laws again are you serious random means normal distribution and small world means power law

Power Laws Again? Are you serious? (Random means normal distribution and small world means power law)

Erdos and bacon numbers

Erdos and Bacon Numbers

  • Small World Networks exhibit strong connections between neighbors (cliques) but information can still travel quickly because of random connections to other highly connected groups of vertices.

  • Erdos/ Bacon numbers: Level of connection in peer-reviewed journal articles or movie credits. Bacon number of 1 means individual acted in same movie as Kevin Bacon. Nearly all actors connected in this way – exhibits characteristics of small world networks.

Is bacon best

Is Bacon Best?

  • “By processing all of the 1.6 million people in the Internet Movie Database I discovered that there are currently 506 people who are better centers than Kevin Bacon!” –Oracle of Bacon website.

  • Compute average Bacon number and compare to others.

Degree distribution of bacon connery numbers for actors in imd

Degree distribution of Bacon / Connery Numbers for Actors in IMD.

Midterm questions

Midterm Questions?

  • Login