Transcript Networks: Gephi (and a little bit of Palladio)

NETWORKS: GEPHI (AND A
LITTLE BIT OF PALLADIO)
@DJWrisley
#RRSI2014, UTSC, May 2014
NETWORKS
THEORY, ANALYSIS,
VISUALIZATION
Network analysis – a term encompassing a wide
variety of practices with applications throughout
(social) scientific and digital humanistic domains
 “Network theory concerns itself with the study of
graphs as a representation of either symmetric
relations or, more generally, of asymmetric
relations between discrete objects.” (Wikipedia, 6
italicized words are mine—all debatable in
humanist circles)
 Network theory has its own conceptual
vocabulary to express relationships between
objects (e.g. betweenness, centrality, density,
path length, modularity) – how can we interpret
these analytical terms for humanities data?
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NETWORK THEORY, ANALYSIS,
VISUALIZATION (2)
Social network analysis SNA looks at
relationships between actors – what is the nature
of interaction?
 Latour adds objects in actor-network theory ANT
– what could the relation of an actor and an
object be?
 Networks once drawn (drawn by MorettiNetwork Theory, Plot Analysis), are now digitally
created and manipulated
 Powerful way of exploring multidimensional
multi-scalar data (Brughmans)
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SOME BEGINNING REMARKS
@ELIJAHMEEKS ON THE NETWORK
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"The network is not a social network or
geographic network or logical network but rather
a primitive object capable of and useful for the
modeling and analysis of relationships between a
wide variety of objects."
<https://dhs.stanford.edu/visualization/morenetworks/>
BASIC PRINCIPLES (1)
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Not all study of networks is quantitative (Brughmans
on Malkin, 2011), just as every mapping is not made
of spatial data onto a map interface
Digital tools for network visualization and analysis
use tabular data (quantification can be a challenge,
metadata adds qualitative contours)
Digital tools like Gephi allow both for networks to be
explored visually, and for static visuals of them to be
exported. It does not allow for sharing.
Data visualization is a kind of “problem-posing”; we
should avoid fetishizing the final visual.
(McCosker/Wilke) – “diagrammatic” thinking
BASIC PRINCIPLES (2)
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Tabular data used by network viz platforms are
of two basic sorts
Nodes (discrete entities in a network, and any fixed
metadata about them – gender, geospatial data)
 Edges (specific instances of relations between nodes)
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NB: Gephi does generate a nodes table if it is missing
(option: create missing nodes)
DIFFERENT TOOLS
NETWORK VISUALIZATION TOOLS
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Gephi (standalone)
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Palladio (web-based)
NetXL
Sci²
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Elijah Meeks
https://dhs.stanford.edu/gephi-workshop/
https://dhs.stanford.edu/visualization/more-networks/*
Non proprietary code – plug ins
Scott Weingart
Indiana MOOC on visualization
Points of comparison: performance, usability, filter,
formats of data ownership, data portability, cost
EXAMPLE 1
NB: Examples Increase in Scale
ONE TEXT
RAMON LLULL, BOOK OF THE LOVER AND THE BELOVED (LATE
13TH) HTTP://WWW.AM.UB.EDU/~JMIRALDA/LLULLTRA.HTML
LLULL DATA SNAPSHOT (@DJWRISLEY
@TRACEY_DH) NODES (LEFT) EDGES (RIGHT)
LLULL’S “SOCIAL” NETWORKS
(@DJWRISLEY @TRACEY_DH)
LLULL “SOCIAL” NETWORKS, MINUS THE
NARRATOR (@DJWRISLEY @TRACEY_DH)
ALGORITHMIC LLULL
Fruchterman
Force Atlas
TINKER TAILOR SOLDIER SPY(@HAYAY44)
TINKER TAILOR, “THE KARLA COLLECTION”
(@HAYAY44)
14TH C MEDIEVAL FRENCH (AROUND JEAN DE
MEUN) – STYLOMETRIC ANALYSIS OF 70+ TEXTS
J RICHARDS (WUPPERTAL – GERMANY)
STYLOMETRIC DATA SAMPLE
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Open file RRSI stylo experiment
250+ TEXTS OF MEDIEVAL FRENCH – TEST
CASE FOR POWER OF STYLOMETRY GIVEN
DIALECTAL DIFFERENCE (@DJWRISLEY AND
RICHARDS)
Parts of gephi interface
 Visualizing the network using the same file
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J
FACEBOOK NETWORKS VIA
@GRANDJEANMARTIN
NETWORKS OF NEWSPAPERS 19TH C VIA
@RYANCORDELL
IMAGE: ORBIS: THE STANFORD
GEOSPATIAL NETWORK MODEL OF THE
ROMAN WORLD
SPACES AND TEXTS – A NETWORK WITH
GEPHI @DJWRISLEY
VIE DE SAINT LOUIS TEXT - PLACE
@DJWRISLEY
JERUSALEM – ASPECTED BY FORM
@DJWRISLEY
PAUL’S EXAMPLES
A BRIEF COMPARISON Gephi
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standalone
open source
Established large user
community = many
plug-ins
Takes a while to learn
Science/humanities use
Not easy to share
without giving away
data
Network statistics
Palladio
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web-based
free
Relatively new
Learning curve low
Specifically for the
humanities
simple to deal with data
multi-dimensionality
Data is not kept
“without any barriers”
PALLADIO EXAMPLE – SPACE IN
LITERATURE
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Live Example using file “distorted VMP data”