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 networks. We will focus on the design and understanding of complex networks through mapping and visual analysis in order to expand your thinking about the network as a creative and critical medium.

For the other parts, view “Creative and critical use of complex networks” and “Reading & Analyzing Network Maps“.

How to map networks?

Here is a step by step guide for mapping relationships.

1. Understanding the field


Who are the dominant actors in the field?

The first question should be what actors there are in the field that you are interested in 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 a fish meal or the fish themselves. In other words, you have to first investigate and list the actants within your field.

2. Detecting the actors and relationships


What are the critical relationships that can scale?

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 four general categories to think about relationships:

Flow RelationshipTransmission Networks
Something actually flows. Water flows, electricity flows, money flows, news flows… Usually physical, and it could be broken like a pipe.

Event relationshipInteraction Networks
The connection is an event, at a specific time. I email you, I buy something, we do an exhibition together… Something passed during a contact. Explicit.

Attribution relationshipAttribution Networks
The 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 relationships Affiliation Networks
The 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.

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 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, the 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.

# Type Name Description Website Location Age
1 Person Sarah Wilson Artist New York 27
2 Person Ahmad Suphi Lawyer Beirut 41
3 Person John Travolta Actor London 52

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.

# Node Type Node Name Edge Type Node Type Node Name Edge Weight
1 Person Sarah Wilson COLLABORATES Person Ahmad Suphi 1
2 Person Ahmad Suphi COLLABORATES Person John Travolta 1
3 Person Sarah Wilson LIKES Person John Travolta 2

Hand drawing the network that you can now see before your eyes, after your attempts to taxonomize 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, get notified when your collective work is updated, reuse existing data by cloning nodes from others’ graphs. Start from “Collaborators” item in your graph’s menu.

Finally, Graph Commons supports asynchronous collaboration, that is to say, you can clone an existing node to your own graph and incrementally expand from there. This way, you can make mashups of existing and new data points. Also, you will get notified when someone clones your nodes so that you can track of its journey and possibly merge back new data points to your graphs and hopefully start new collaborations. Try cloning in the “Add Node” dialogue, while adding new nodes into your graph.

The Graph Commons Interface

Get started with short tutorial videos, the first one is below. See the other video tutorials here.


We hope that you will spend some time browsing current graphs and creating new ones on Graph Commons. Feel free to share anything you create or find.

We welcome feedback on your experience. You can send all problems, suggestions, questions and comments to, we’d love to hear from you.

–Graph Commons Team