Visualizing cultural networksChris Alen SulaSchool of Information & Library Science, Pratt Institute

4 Apr 2013 – Visual Resources AssociationSession #11: Cultural Heritage Data Visualizations

Overview

‣Case study: Occupy Wall Street Project List

‣What networks can represent

‣Data structure for networks

‣Gephi network software

‣A few recipes

Cultural heritage documents often contain information about relationships—the types of relationships that can be explored and studied through network visualization.

Case study: Occupy Wall Street Project List

Project information

Structured data

DAP <—> Occupy Town SquareDAP <—> OWS Direct ActionDAP <—> OWS Silkscreen Guild . . .Occupy Town Square <—> OWS Direct ActionOccupy Town Square <—> OWS Silkscreen Guild . . .

“less than a year after the last protester was removed from New York City's

Zuccotti Park, the movement has re-emerged as a series of laser-focused

advocacy groups that, loosely organized under the Occupy umbrella, are trying to

effect change in a variety of sectors, financial and otherwise.”

Time Magazine, Dec 3, 2012

Case study: Occupy Wall Street Project List

Feb 2012 Apr/May 2012 June/July 2012

What networks can represent‣ kinship and personal connections (friends,

partners, co-performers, colleagues, acquaintances)

‣ organizations (roles, partnerships, alliances)

‣ linguistic associations (words, topics)

‣ trade routes, voyages, infrastructure

‣ communication (letters, social media)

‣ ideological ties (claims, theories)

‣ In each case, we look for similar things (nodes) which are related in regular ways (edges)

Data structure for networks‣edge table*

‣source* / target* / direction / weight / timespan

‣node table‣id* / name* / property 1 / property 2 / etc.

*required

Data structure for networks‣Look for consistently recorded information

(recurring entities, similar connections)

‣Think hard about whether connections are directed or undirected—this may change the structure

‣Expect that you’ll have to resolve some problems with messy data (ambiguity, variant spellings, etc.)

‣Search for familiar real-world objects to make into nodes (e.g., people) and treat relationships between them as edges (e.g., being displayed together at the same gallery event)

Nodes v. Edges

‣ Networks can be single mode (one type of node, e.g., people) or multi-mode (e.g., people and institutions)

‣ Most layouts and network statistics are built for single mode networks, including ones in Gephi

‣ In many cases, it is advantageous to

‣ attribute-ize node properties (e.g., make ‘person’ and ‘institution’ values in a ‘type’ attribute)

‣ edge-ize abstract “things” that actually only connect nodes (e.g., being included in the same catalog)

‣ rather than treating everything as a new type of node.

Gephi network software

‣ free and open-source for Windows, Mac, and Linux

‣ allows for detailed design adjustments (sizing, coloring, filtering, labeling)

‣ community-developed plugins provide additional layout options and customization

‣ computes network statistics, detects clusters

‣ exports files as image (PNG, PDF), vector (SVG), or web formats

http://gephi.org

A few recipes

‣CategorizationMap terms that appear throughout a subject vocabulary by treating hierarchy as an edge and each term as a node. (Reveals conceptual structure of categorization system.)

‣CollaborationMap people that collaborate on works by treating each person as a node and each instance of collaboration (i.e., work) as an edge that connects them. (Reveals social patterns.)

‣ProvenanceMap provenance relations between works by treating each work as a node and co-location/co-exhibition as a relationship that connects them. (Reveals curitorial relationships.)

Contact

Chris Alen SulaAssistant ProfessorPratt Institute, School of Information & Library Sciencehttp://chrisalensula.org@chrisalensula on Twittercsula@pratt.edu