Network Visualisation & Analysis

Making the invisible visible

Network visualisation is a powerful tool to explore the relationships and dynamics that exist within a network, which are otherwise hidden.

It enables the network to see itself.

What can it show us?

The central elements

Who are the key actors, most connected individuals/organisations etc.? Are there central information hubs (e.g. highly connected individuals) in the system which are outside of the formal channels?

Blind spots and edges

Are there clusters (forming) in the network? If so, how could they benefit from each other and are there already bridges between them? What are hidden themes that are relevant to address or strengthen? How can we create a frame around the diversity of actors and enable innovation?

Leverage points for transformation

Where are the most promising entry points for triggering systematic change? Are there ways to create synergies between actors, including collaborations or resource sharing? How is it possible to strengthen the quality of connections and build trust?

The value of network visualisation


For Network Organisers

  • Strategic and participatory event planning, including embedding single events into broader context
  • Visualising existing engagement in the network
  • New approach to evaluating success and impact
  • Leverage the resources and motivation that already exists within the network
  • Understand what is “below the surface” of interactions between actors


For Network Actors

  • Provide practical and actionable guidance in how to benefit from the network (e.g. accessing skills or knowledge of others)
  • Provide opportunity for actors in the network to see themselves within the broader context, thereby making the contribution of each actor visible.
  • Foster strategic and intrinsically motivated exchange of ideas, skills and resources


For Externals

  • Showcase what the network and its members stand for
  • Spark curiosity and create interest for externals to join or support the network
  • Visualise and measure impact and report on actions in a meaningful way
  • Create clarity on how to engage and partner up with the network or its actors


Working out what to visualise

The first step to creating a network visualisation is conceptualising what to visualise and why. There are three main components in a map: nodes, connections and clusters.

Nodes & Actors


  • Individuals
  • Organisations
  • Projects



  • Between nodes (actors)
  • Individuals to organisations or themes
  • Type of connections: e.g. collaborations, partnerships, customers
  • Strength of connections: e.g. level of interaction or formalisation



  • Relevant thematic areas (e.g., Sustainable Development Goals)
  • Skills and resources (existing and needed)
  • Geographical location and distribution
  • Sustainable Development Goals
  • Strategic work streams or objectives

Core questions include: who will be the main user of the map? Who are the nodes (actors) to include in the visualisation and what are the key clusters? Will it be used for internal or external purposes, or both? Do we want to map individuals and the connections between them? Who are the nodes (actors) to include in the visualisation and what are the key clusters?

The answers to these questions determine the kind of data we collect (or if it is possible to use existing data), the methods and software we use to collect it (e.g. in a centralised or decentralised way), the way we visualise it, and the kind of database needed to store data. Many networks already have existing databases which can be used, depending on the kind of information they want to visualise, yet looking into additional ways to access or collect data can enrich the visualisation and analysis. This step is crucial as the quality of the data determines the quality of the network visualisation and analysis.




Intentional connections

Depending on the kind of data collected, it is possible to use network visualisations to intentionally create connections between people or organisations.

This can work in two ways:

  1. Using the search function in the network map to search for specific tags manually (e.g. skills, themes, etc.)
  2. Using Natural Language Processing to create automated suggestions.

Case Studies

Global Festival of Action for the Sustainable Development Goals

The Global Festival of Action, organised by the UN SDG Global Action Campaign, is a three day festival which brings together diverse stakeholders working towards the Sustainable Development Goals. As an Action Partner of the Festival in May 2019, the Unity Effect team conducted a live social network mapping of 227 festival participants.

The network map shows the connections between participants as well as the SDGs each person is working on (maximum 3 per person), skills offered and skills needed.

The purpose of visualising the network was to 1) allow participants to connect and collaborate more deliberately throughout and beyond the festival, and 2) provide a way for  the system to see itself; to identify blind spots, opportunities for strategic planning and action, and leverage points for change.

The map provided insights into which SDGs are most and least selected. It visualised the overlaps (or lack thereof) between different SDGs, where multiple people were working on the same combination of SDGs. These overlaps can be seen as potential spaces for innovation. Further, it highlighted the skills which are offered and needed in the network and where there is a discrepancy between the demand and availability of certain skills. This data could be used for planning of future events as well as taking strategic action from a systems perspective.

Strategic Planning and Monitoring

More classical approaches to impact focus on the visible elements of a system, and suggest linear pathways to achieve change.

From our perspective, the invisible components (e.g. development of trust in a system or the inner attitude of people) are equally important, and impact should be seen as systemic and multi-dimensional change over time.

Yet, history has shown over and over again that there is no linear and predictable way to create social change. Oftentimes, phases of seeming stability (e.g. in a political context) lead to rapid and unforeseen shifts. In a similar way, there can be many pathways towards the impact we desire to create. Yet we often can’t know which of these pathways will be successful in a dynamic and interrelated world. We can, however, understand the underlying social dynamics and the best possible conditions (e.g. resources & support) which enable it.

This approach to impact is also an invitation to re-think power dynamics. Usually it is the intervener defining the desired impact. With a systems approach it is increasingly important to listen to the affected people and see change as a co-creative effort.


Working with questions instead of answers

It can be powerful to see impact as a joint exploration. For this, working with questions is more promising than defining answers. For example:

  • Where are resources within and outside a network (or system) that we can leverage?
  • Is the network healthy? Where are blind spots and spaces for innovation?
  • Are new connections actually leading to collaboration and joint action?
  • Do individuals within the network develop inner (leadership) capacities to implement and sustain strategic action?
  • Do actions lead to a shift in mindset and mental models within the broader environment?
  • Which outside influences are at play? Is the network (or system) resilient to those influences?

Below the surface of the map

The most substantial work required to create an interactive visualisation or in-depth analysis consists of data clean-up and restructuring to fit the “element-relationship” model. This usually implies rethinking the present database model from a standard relational database management system (RDBMS) to a more powerful graph data model (Graph Database).

Based on such a data model, a visualisation can include several “views” of the same map, by including filtering and clustering options that allow users to refine the elements and the relationships between them.

Working with a graph database model also enables the network to draw on methods like natural language processing (NLP) - a form of artificial intelligence. NLP conducts semantic analysis (e.g. of profiles of people or written documents) and enables powerful features like match-making or a deep dive into the relevant (hidden) themes of the network.

As Unity Effect, we work with experts in the field of digital humanities, graph database models and natural language processing and translate those approaches and concepts into relevant and actionable results.