Network Maps for End Users: Collect, Analyze, Visualize and Communicate Network Insights with Zero Coding

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A project from the Social Media Research Founda8on: h:p://www.smrfounda8on.org

Network Maps for

End Users:

Collect, Analyze,

Visualize and

Communicate

Network Insights with

Zero Coding

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About Me

Introduc8ons

Marc A. Smith

Chief Social Scien8st

Connected Ac8on Consul8ng Group

[email protected]

h:p://www.connectedac8on.net

h:p://www.codeplex.com/nodexl

h:p://www.twi:er.com/marc_smith

h:p://delicious.com/marc_smith/Paper

h:p://www.ﬂickr.com/photos/marc_smith

h:p://www.facebook.com/marc.smith.sociologist

h:p://www.linkedin.com/in/marcasmith

h:p://www.slideshare.net/Marc_A_Smith

h:p://www.smrfounda8on.org

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Central tenet

–  Social structure emerges from

–  the aggregate of rela8onships (8es)

–  among members of a popula8on

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Phenomena of interest

–  Emergence of cliques and clusters

–  from pa:erns of rela8onships

–  Centrality (core), periphery (isolates),

–  betweenness

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Methods

–  Surveys, interviews, observa8ons, log ﬁle analysis, computa8onal analysis of matrices

(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communica8on, Simon Fraser University. pp. 7-‐16

Social Network Theory

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SNA 101

•  Node

–  “actor” on which rela8onships act; 1-‐mode versus 2-‐mode networks

•  Edge

–  Rela8onship connec8ng nodes; can be direc8onal

•  Cohesive Sub-‐Group

–  Well-‐connected group; clique; cluster

•  Key Metrics

–  Centrality (group or individual measure)

• Number of direct connec8ons that individuals have with others in the group (usually look at incoming connec8ons only)

• Measure at the individual node or group level

–  Cohesion (group measure)

• Ease with which a network can connect

• Aggregate measure of shortest path between each node pair at network level reﬂects average distance

–  Density (group measure)

• Robustness of the network

• Number of connec8ons that exist in the group out of 100% possible

–  Betweenness (individual measure)

• # shortest paths between each node pair that a node is on • Measure at the individual node level

•  Node roles

–  Peripheral – below average centrality

–  Central connector – above average centrality

–  Broker – above average betweenness

E D F A C B H G I C D E A B D E

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Email (and more) is

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PaDerns are leE behind

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There are many kinds of 8es….

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World Wide Web

Each contains one or more social networks

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Welser, Howard T., Eric Gleave, Danyel Fisher,

and Marc Smith. 2007.

Visualizing the Signatures of Social Roles in

Online Discussion Groups

.

The

Journal of Social Structure

. 8(2).

Experts and “Answer People”

Discussion starters, Topic se:ers

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Tag Ecologies I

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HUB-‐AND-‐SPOKE OF DECEIT: When Enron employees communicated about legi8mate projects, e-‐mails were reciprocal and informa8on was shared widely (right), but

communica8ons about an illicit project (les) reveal a sparse network with a central, informed clique and isolated external players.

Brandy Aven, CMU

h:p://www.sciencenews.org/view/generic/id/330731/8tle/ Informa8on_ﬂow_can_reveal_dirty_deeds

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Goal: Make SNA easier

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Exis8ng Social Network Tools are challenging

for many novice users

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Tools like Excel are widely used

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Leveraging a spreadsheet as a host for SNA

lowers barriers to network data analysis and

display

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Who we are

People Disciplines InsGtuGons

University

Faculty Computer Science University of Maryland Students HCI, CSCW Oxford Internet Ins8tute

Industry Machine Learning Stanford University Independent Informa8on

Visualiza8on Microsos Research Researchers UI/UX Illinois Ins8tute of

Technology Developers Social Science/Sociology Connected Ac8on

Network Analysis Cornell

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What we are trying to do:

Open Tools, Open Data, Open Scholarship

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Build the “

Firefox of GraphML

” – open tools for

collec8ng and visualizing social media data

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Connect users to network analysis – make

network charts as easy as making a pie chart

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Connect researchers to social media data sources

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Archive: Be the “Allen Very Large Telescope

Array” for Social Media data – coordinate and

aggregate the results of many user’s data

collec8on and analysis

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Create open access research papers & ﬁndings

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Make “collec3ons of connec3ons” easy for users

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What we have done:

Open Tools

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NodeXL

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Data providers (“spigots”)

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ThreadMill Message Board

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Exchange Enterprise Email

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Voson Hyperlink

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SharePoint

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Facebook

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Twi:er

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YouTube

–

Flickr

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What we have done:

Open Data

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NodeXLGraphGallery.org

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User generated collec8on

of network graphs,

datasets and annota8ons

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Collec8ve repository for

the research community

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Published collec8ons of

data from a range of social

media data sources to help

students and researchers

connect with data of

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Social Media Research Founda8on

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He ath er _has h igh be_tw ee nn es_s

NodeXL

Network Overview Discovery and ExploraGon add-‐in for Excel 2007/2010

A minimal network can illustrate the ways diﬀerent loca8ons have diﬀerent values

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h:p://www.connectedac8on.net/2010/04/25/bernie-‐hogans-‐facebook-‐social-‐network-‐data-‐ provider-‐and-‐visualiza8on-‐toolkit/

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Network of connec8ons among the people who tweeted the term “PAWCON” on 19 October 2011

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Analogy: Clusters Are Occluded

Hard to count nodes, clusters

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Social networks in TwiDer among people with at least one connecGon to someone else who Tweeted “Obama” on January 25, 2011

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Network of word pairs frequently men8ons among people who Tweeted the name “Obama” on January 25, 2011

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What we want to do:

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Build the tools to) map the social web

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Move NodeXL to the web:

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Node for Google Doc Spreadsheets!

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WebGL Canvas

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Connect to more data sources of interest:

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RDF, MediaWikis, Gmail, NYT, Cita8on Networks

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Solve hard network manipula8on UI problems:

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Modal transform, Time series, Automated layouts

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Grow and maintain archives of social media network data sets for

research use.

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Improve network science educa8on:

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Workshops on social media network analysis

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Live lectures and presenta8ons

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Work Items

Autoﬁll Group A:ribute

Merge Edges by A:ribute

Modal Transform

Merge Workbooks

Automated Dynamic Filters: Time Series Analysis, contrast

Cap8ons and Legends

Upload to Graph Gallery++: cap8ons, workbook

Graph Gallery++

User Accounts, Repor8ng, RSS Feeds,

Network Visualiza8on Web Canvas

Import: RDF, Wiki, SharePoint, Keyword networks from text

Metrics: Triad Census

Layouts:

Force Atlas 2, Lin Log, “Bakshy Plots”, Quality Measures

Query-‐by-‐example search for network structures

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How you can help

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Sponsor a feature

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Sponsor Webshop 2012

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Sponsor a student

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Schedule training

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Sponsor the founda8on

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Donate your money, code, computa8on, storage,

bandwidth, data or employee’s 8me

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Help promote the work of the Social Media

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Contact: