+

Big Data, Network AnalysisWeek 13

+How is date being used

Predict Presidential Election - Nate Silver – http://adage.com/article/campaign-trail/nate-silver-s-election-predictions-a-win-big-data-york-times/238182/

Predict Pregnancy - Target – http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

+Why Networks?

Why is the role of networks in CS, Info Science, Social Science, Physics, Economics, and Biology expanding?More DataRise of the Web and Social MediaShared vocabulary between (very

different fields)

+Reasoning about Networks

How do we reason about networks? Empirical: Look at large networks and see what we find Mathematical Models: probabilistic, graph theory Algorithms for analyzing graphs

What do we hope to achieve from the networks? Patterns and statistical properties of network data Design principles and models Understand why networks are organized the way they are

(predict behaviors or networked systems)

+Why networks?

Network data is increasingly available:Large on-line computing applications

where data can naturally be represented as a network Online communities: Facebook Communications: Instant

MessengerNews and Social Media: Blogging

Also in systems biology, health, medicine, …

+Networks: Rich Data

a – Internetb – Citation networkc – World Wide Web

d – sexual networke – dating network

+Networks Information networks:

World Wide Web: hyperlinks

Citation Blog

Social networks: Organizational Communication Collaboration Sexual Collaboration

Technological networks: Power grid Airline, road, river Telephone Internet Autonomous systems

+What is Social Network Analysis?

Network analysis is the study of social relations among a set of actors. It is a field of study, not just a method.

“Social network analysis involves theorizing, model building and empirical research focused on uncovering the patterning of links among actors. It is concerned also with uncovering the antecedents and consequences of recurrent patterns.” (Linton Freeman)

+The Network Perspective

People(nodes)

Ties (edges)

+Ties in a social network

Directed or undirected

Simplex or multiplex

Valued or unvalued7

+ What is a Social Network?

A set of dyadic ties, all of the same type, among a set of actorsActors can be persons, organizations,

groupsA tie is an instance of a specific social

relationship

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+ Network Relations

Among Individuals Kinship Role-based (friend of) Cognitive/Perceptual (knows, aware of) Affiliations Affective (likes, trusts) Communication

Among Organizations Buy from / Sell to Owns shares of Joint ventures

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+Key Perspectives in Social Network Analysis

Focus on relationships between actors rather than just the attributes of actors.

Interdependent view rather than atomistic (individualist) view of social processes and effects.

Social structure affects substantive outcomes

(which is a philosophical departure from other traditions)

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+Interdisciplinary Field of Study

Computer Science Designing and understanding complex network

structures

Mathematics, Physics Methods, complex systems analysis

Social Science (Sociology, Social Psychology, Economics) Theories and measurement of social networks,

using networks to understand human behavior

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+Multiple Levels of Analysis

Individual Level How does individual position in a network

affect various outcomes for the individual?

Systems Level How does the network structure as a whole

affect outcomes for various tasks?

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+ Network Data Collection

Common Types: Survey Interviews Affiliation/

membership records Behavioral (e.g.,

observation of communication patterns)

Experiments

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Data obtained through manyeyes and graphed: http://www.esv.org/blog/2007/01/mapping.nt.social.networks

+ Types of Network Data

One mode Two mode

Whole network Egocentric

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A B

C

A B

School A

+Non-directed versus Directed Graphs

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A B

C

A B

C

Analyzing Social Networks

A B C D

A - 1 1 1

B 1 - 1 0

C 1 1 - 1

D 1 0 1 -

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A

DB

C

Simple Adjacency Matrix

+Some Key Principles in Social NetworksDegree

The degree to which actors are connected directly to each other by cohesive bonds

Density The proportion of direct ties in a network relative

to the total number possible

Centrality a group of metrics that aim to quantify the

"importance" or "influence" (in a variety of senses) of a particular node (or group) within a network

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+ Degree in Social Networks

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+Density in Social Networks

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Low Density High Density / Integrated

“Radial” (Valente)

+ Centrality in Social Networks

Degree Centrality

Closeness Centrality

Betweeness Centrality

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+ Why all of this sudden interest?

The strength of the “Strength of Weak Ties” argument. Granovetter (1973)

Argues that ‘weaker’ peripheral ties build heterogeneous networks, which in turn provide access to new and useful information.

Heterogeneity through weak-ties widely accepted as a “good thing” for communication Access to jobs Access to other opportunities Helps distribute ideas, innovations

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+Ted Talk

The hidden influence of social networks http://youtu.be/2U-tOghblfE

+Social Networks

http://www.youtube.com/watch?v=5etSid8G6EU

http://www.youtube.com/watch?v=PThAriHjk10&playnext=1&list=PL05CC28C66163B00D&feature=results_main

+Slides adapted from:

Jure Leskovec, Stanford CS224W: Social and Information Network Analysis ure Leskovec, Stanford CS224W: Social and Information Network Analysis

http://bit.ly/Y7fALp