NETWORK ANALYSIS RESOURCES
Network analysis allows you to explore and visualize relationships in your data. For humanists, this most often means social networks of some kind, whether musicians and patrons in medieval Venice or retweets on Twitter.
NETWORK ANALYSIS RESOURCES
GETTING STARTED
Desktop opensource solutions (intermediate/advanced)
Gephi, Cytoscape
Web-based solutions (collaborative, classroom-geared)
Net.Create (ask for more info)
Programming solutions (advanced)
Python (with package networkx)
nodes — points in the network graph
edges — connections between points
ego — the one node you're currently interested in
weight — the number of connections between two nodes
network density — a measure of how connected a network is
centrality — the importance of a specific node in the overall network
directed vs. undirected — whether or not there is a directionality between the connections in a network
unimodal vs. bi/multimodal — the number of different types of nodes in a network (i.e. people only versus people and institutions)
Musicians in Venice
http://musiciansinvenice.com/dissertation/
Networks of Performance and Patronage: Russian Artists in American Dance, Vaudeville, and Opera, 1909-1947
http://networksalarusse.com/network/
PROCESS OVERVIEW
Network analysis almost always begins with collecting and structuring your data in a format that one of the software tools can read. At IDAH, we mainly support the open source software Gephi. Getting your data ready for this and other software could like something like this:
Identify kind of relationships you have. In your data, decide what type or types of entities you will be connecting. (The entities are referred to as nodes and the connections edges.) Are there different types of nodes in your data (i.e. individuals and institutions)? Is the connection a simple or two-way one (undirected), or does one node initiate the connection (directed)? How will you label these nodes and what other information might you want to include about either the nodes or the edges?
Structure your data in spreadsheet form. A software like Gephi will ultimately want two related spreadsheets, one with a list of nodes and one with a list edges. But if you are digging through source material to compile this information, often it is easier to build just the edges sheet. At a minimum this will mean filling in a "source" column and a "target" column (those terms are used for any kind of network graph, but they have a special meaning when the graph is a directed one). If there is a large amount of data you need to add for each node, then you may want to build these sheets simultaneously. Ultimately, Gephi will want numbered IDs for each node and ID relationships in the edge table. This is easy enough to generate in Excel after you've added your data, so long as you've been careful to make sure that each node name is unique.
Load your data into a network analysis software and analyze. See tutorials below for how to do this in Gephi and for recommendations on other tool options.
TUTORIALS AND OTHER RESOURCES
Gephi needs two spreadsheets: nodes and edges. Nodes refers to the individual entities in your network—the points. Edges are the relationships between the nodes.
The nodes sheet should contain:
Label | ID | NodeType (optional) | Other (optional) |
Sarah Smith | 1 | scholar | |
Plos One | 2 | journal |
The edges sheet should contain:
Source* | SourceLabel | Target | TargetLabel | Other (optional) |
1 | Sarah Smith | 2 | Plos One |
*If there is directionality in the relationships, this should be reflected in where entities appear in the "Source" or "Target" columns. If there is no directionality (Gephi calls this "undirected"), then it does not matter in which column an entity is placed, only that there is a relationship.
You can easily create the two spreadsheets that Gephi requires from a single spreadsheet of relationships. At minimum, the spreadsheet you start with needs to contain one column for one entity and another column for the entity it relates to. In this scenario, each row is one instance of a connection (i.e. a scholar publishes an article in a journal). Using Excel, your next steps could look something like this:
Create a new sheet called "nodes." Rename the original sheet "edges."
From "edges" copy both columns of the connected entities paste them into the single column "Label" in "nodes," then run "Remove Duplicates" to make sure each entity appears only once. (Be sure to copy over any additional information you need for each node, such as node type, dates, etc.)
Create IDs for each of the nodes in the "ID" column.
In "edges" rename existing columns and create new ones to correspond to the above formatting.
Use VLOOKUP to insert the node ID for each node when it appears in "SourceLabel" or "TargetLabel" in "edges."
Once all the IDs are imported, do a copy and "paste as value" for both "Source" and "Target."
If all of the data and IDs seem to be present in both sheets, then you are ready to load these sheets into Gephi. You can export each sheet as a separate CSV file, or load the Excel file as is and select the appropriate sheets in Gephi.
Open Gephi and select "New Project."
In the top left, select "Data Laboratory"
Select "Import Spreadsheet" and select your nodes CSV or Excel file.
Make sure the sheet is being imported appropriately (as either nodes or edges), then click "Next."
Click "Finish."
Change "Graph Type" to the direction type (most often "Undirected").
Select "Append to existing workspace," then click "OK."
Repeat steps 3–7 for your edges file.
In top left, selected "Overview" to view and analyze your network.
Once your network data is loaded into Gephi, here is an overview of the most common things you might want to do with it. All of these options are visible in the "Overview" screen which can be selected at the top left.
Appearance (top left)
At top left—select to style either nodes or edges
At top right—select for color, size (nodes only), label color, label size
Unique—set a hardcoded color or size
Partition—set based on a category in your data
Ranking—set based on a number range (often something generated in Gephi, such as degree)
Layout (bottom left)
Start with Force Atlas
Then run Yifan Hu Proportional
Play with other layouts, but the above two may be sufficient
Random Layout resets graph
Left-Side-of-Graph-Screen Controls
Most useful is magnifying glass icon near bottom that recenters the graph
Tools below magnifying glass reset other elements
Tools at top of menu allow various kinds of selection and editing
Bottom-of-Graph-Screen Controls
Bold "T" switches node labels on and off
Light "T" switches edge labels on and off
Standard font controls to the right
Statistics (right)
Modularity identifies network neighborhoods—after it is run you can partition based on modularity class
Read about or experiment with other statistics options
Filters (right)
The basic procedure is to pull a filter down into Queries, select settings, then click "Filter"
Select "Stop" to turn filter off
More complex to work with because of many options that can be used both independently or nested
A common filter, and good place to start, is Attributes>Partition—then select a category to filter by
Data Laboratory
You can view the nodes and edges tables by selecting "Data Laboratory" at the top left. You can also right click on a node and select "Select in data laboratory" to view that node's record in the nodes table.
Download Gephi here: https://gephi.org/.
Find tutorials and examples here: https://gephi.org/users/.
Gephi also comes with a few sets of sample data that can be loaded on start up.