Mapping Networks
A useful guide for mapping and understanding relationship diagrams
Graph Commons is happy to share the network mapping workshop notes with you. Below, you will find a useful guide, conceptual and practical insights for making and understanding network maps. This is the second part of a 3-part guide on mapping, understanding, and analyzing complex networks. For the other parts, view “Creative and critical use of complex networks” and “Analyzing Data-Networks“.
1. Understanding the field
The first question should be what actors there are in the field that you are interested to research. The actors can vary from real persons to concepts, from institutions to inanimate objects. Let’s say that a researcher is interested in fish farms, the market relations between the farms and the vendors and how this ecosystem of fishing operates in its processual cycle. The reproduction of fish then would not only rely upon the human subjects in the field, but also would rely on companies that produce fish meal or the fish themselves. In other words, you have to first investigate and list the actants within your field.
Who are the dominant actors in the field?
2. Detecting the actors and relationships
The second step is to come up with relations that make the interaction possible between the actors. These could be from interactions like “sending email”, “collaborating”, “influencing”, to affiliations such as “being a member”, “belonging to a category”, “similarity”. For instance, if one is interested in understanding the lobbying activities of a certain push group, one would expect to find official and as well as organic links that make up the bigger social network. Or another relation one would expect to detect would be bribery, which is somewhat neither organic nor official. It is therefore good idea to start with an educated guess of what kind of relations that one can encounter. However, it is also vital not to assume the categories beforehand and overlook the ones that we are not deliberately pursuing. Also remember that the number of actors can also be limited by your definition of your field and research topic.
There are roughly 4 general categories to think about relationships:
Transmission Networks — Connection is something that actually flows: water flows, electricity flows, money flows, news flows… Usually a physical linkage that could be broken like a pipe.
Interaction Networks — Connection is an event, with a specific time. I email you, I buy something, we do an exhibition together… Something passed during a contact. Explicit.
Attribution Networks — Connection is an expression of a relationship. You are my friend, I love you, you trust him, she recognizes you… Visible only if you state it.
Affiliation Networks — Connection is a belonging to a group or category. We are in the same school, things are in the same category, organizations connected by board members… Linked by correlation, similarity, or membership. Implicit.
The relationships you choose will more or less fall into one of these categories. No need to say, these categories are here to give you a guidance to start thinking about the relationships, you can get creative and introduce relationships out of these categories. At the end creativity is about connecting something to something else in unexpected ways, right.
What are the critical relationships that can scale?
3. Compiling data & making the map
Start gathering data after you listed the actor and relation types. The best way to organize your data is to put it into a spreadsheet. Use the Graph Commons “Data Table” feature, which turns your data into a self-organizing diagram as you edit. In fact, you can visually add nodes and relations by just clicking on the map canvas.
Alternatively, you can use the Graph Commons Google Spreadsheet Template to organize your edges and nodes for import. If you prefer the external spreadsheet option, you will find two separate sheets in the template, one sheet for nodes, second sheet for relationships, which will have a column structure like below.
Nodes Table
Simply a list of nodes at each row and their properties at each column.
Edges Table
A list of relations. At each row, on the left, “from” node types and names, on the right “to” node type and names, at the center a single column Edge Type to represent the relationship in between. Also add weight if you need to.
Hand drawing the network that you can now see before your eyes, after your attempts of taxonomization of the field, helps a lot. So, start drawing circles, writing names and connecting them with lines, where you can generate a sketch for your network map. This way the nodes and edges will appear to you as they have been slowly while you were listing the actors and relationships.
Your hand-made diagram will get messy pretty quickly. So you need to transfer your work to a computer simulation, where things can get organized. The network map on Graph Commons is a self-organizing physics simulation software, where the layout is organized based on the connections, showing both similarities and differences between nodes, highlighting central as well as peripheral actors, and revealing organic clusters that you could not see normally.
4. Collaboration on Graph Commons
We get the most out of mapping when we interconnect our partial knowledge about an issue and build a bigger picture together. Invite collaborators to your graphs via email or usernames if they are already signed up. And get notified when your collective work is updated. Start from the “Collaborators” panel in your graph’s menu.
Finally, Graph Commons supports asynchronous collaboration, that is to say, you can clone a public node while adding a node to your own graph and incrementally expand your graph from there. This way, you can make mashups of existing and new data points and collectively experiment in the act of mapping as an ongoing practice.
The Graph Commons Interface
We prepared short video tutorials to get you started with Graph Commons. The first one below simply shows how to browse an interactive graph, the second one shows how to make a graph starting from a new blank canvas.
We suggest you go to Graph Commons yourself, create an account, and try our interactive tutorial that will get you started step by step with the interface.
We hope that you will spend some time browsing current graphs and creating new ones. Feel free to share anything you create or find.
We’d love to hear your feedback at contact@graphcommons.com. Follow @graphcommons on Twitter, subscribe to Graph Commons Journal on Medium, join our Slack chat channel for discussions.
This is the second part of a 3-part guide on mapping, understanding, and analyzing complex networks. For the other parts, view “Creative and critical use of complex networks” and “Analyzing Data Networks“
