Learning outcomes:
Understand the purpose of the Influencer Network Graph
Learn how to interpret the graph to identify highly-connected individuals
Discover how to use the graph to gain insights into the spread of subjects, ideas and content.
What is the Influencer Network?
One of Pulsar’s USPs is our advanced influencer network graph, which is a live, browser-based engagement graph mapping the way people engage with each other, across any data source that provides us with an engagement relationship between two or multiple authors (e.g. X, Instagram, Facebook, etc). By using our Influencer Network graph you can see who is reacting to whom, and therefore who is central to spreading a certain subject, idea, content etc.
What’s also unique about our influencer network graph analysis is that the graph is not cached but updated as new data comes in and any filter applied to the dataset will be reflected in the network graph visualisation. You can also view this chart in 2D or in 3D.
3D versus 2D
The immersive 3D perspective adds a new layer of depth to your audience analysis in Pulsar, allowing you to explore the network's structure in a more intuitive and revealing way. By visualising networks in three dimensions, users can explore structure and influence more intuitively. The added depth helps surface clusters and connections with greater clarity, revealing nuanced relationships that can be difficult or sometimes impossible to spot in a flat 2D view. Communities become layered and spatial, transforming a complex web of interactions into a tangible, explorable landscape of influence.
By visualising influence as a living 3D network, you can move beyond surface-level insights and into real world audience intelligence networks. Rotate, zoom, and explore the 3D network from multiple angles to uncover patterns that were once hidden in flat views.
Influencer Network: 2D View
Influencer Network: 3D View
Understanding the Influencer Network Graph
At its core, the Influencer Network maps post & engagement relationships across any supported data source in TRAC. This can be X, Youtube, Facebook, etc.
Nodes represent authors and accounts.
Connections (Edges) represent engagements such as comments, replies, and reposts.
Node size reflects the volume of engagement an author generates.
Clusters emerge where audiences interact most frequently.
The more engagement an author attracts, the more connected and therefore influential they are. This makes highly connected voices immediately stand out, even in large, noisy conversations. The graph also automatically groups users into colour-coded clusters. This segmentation reveals distinct tribes and sub-communities, enabling you to tailor your messaging for maximum resonance.
Why Influence as a Network Matters
Traditional metrics flatten influence into engagement counts and rankings. Our powerful influencer network visualisation shows structure. With this graph, you can:
Spot true conversation drivers, not just the loud voices.
Understand how audiences cluster and overlap.
Identify bridges between communities and narratives.
See how influence flows, rather than assuming where it sits.
So instead of asking “Who has the biggest following?”, you can now ask: “Who actually activates this audience?”
Getting the most out of The Influencer Network
For Analysts: See influence as a system, not a score
The Influencer Network Graph gives analysts a structural view of engagement, revealing how conversations form, cluster, and spread. Instead of relying solely on volume metrics, the network shows explicit post-to-engagement relationships, making it easier to identify:
Highly connected authors driving disproportionate engagement.
Distinct audience clusters and sub-communities.
Bridge accounts linking otherwise separate groups.
Dense interaction patterns that signal coordination or amplification.
The new 3D view is particularly valuable for complex datasets, where overlapping communities and high engagement volumes can obscure meaningful relationships in flat 2D views. By adding depth, analysts can disentangle networks, explore interaction layers, and surface patterns that would otherwise remain hidden.
For Comms Teams: Know who really moves the conversation
Not everyone with a big following actually drives engagement. The Influencer Network Graph helps comms teams quickly see who audiences respond to, not just who posts the most.
By visualising who reacts to whom, the graph highlights:
Voices that spark conversation and amplification.
Natural advocates (or detractors), and organic amplifiers.
Communities forming around specific narratives or moments.
How messages travel through different audience groups
Whilst the 2D view makes it easy to identify key influencers at a glance, the new 3D view reveals how different communities connect, helping teams tailor outreach, refine messaging, and understand where their stories gain traction.
For Insight Teams: Turn audience behaviour into understanding
The Influencer Network Graph helps insight teams move beyond engagement totals to understand how audiences interact and organise themselves.
By mapping engagement relationships, the network reveals:
How audiences cluster around people, topics, or narratives
Which voices anchor different communities
Where conversations overlap and where they don’t
How influence shifts over time or across themes
The 3D view adds critical context by showing depth and proximity between groups, helping insight teams explore complex audience ecosystems and uncover emergent patterns that inform strategy, research, and long-term planning.
One network, multiple perspectives. Whether you’re validating influence, shaping communication strategy, or uncovering audience dynamics, the Influencer Network Graph in TRAC adapts to how your team thinks and what you need to learn.
💡 Top Tip: You can view these highly-connected individuals and the posts that they're contributing to the conversation by clicking on the nodes, as demonstrated in the screen recording below.
You can export the network graph as a PNG file, XLS, or SVG file as shown below.
Gephi Integration
In addition to this, Pulsar is the only social listening platform that retains a network graph model of any dataset it analyses and allows the user to export the entire dataset as a graph for independent analysis and visualisation. This can be done via our Gephi integration. Gephi is an open-source software application for visualising and analysing large networks and graphs. It provides a user-friendly interface for creating and manipulating complex network graphs, as well as a suite of tools and algorithms for exploring and analysing the structure of these networks.
For users with limited machine capacity, Pulsar also provides an option to sample down the graph to a manageable size, while retaining the key features of its complexity. The sampling algorithm uses betweenness centrality as the key parameter for downsizing the graph. This allows the user to analyse and visualise the graph on their own machines, outside of Pulsar.
The export option for the Gephi can be found on the top-right of the Influencers Network chart, as shown below.
We hope you enjoyed reading this article! 📚
If you have any questions or would like to learn more, please don't hesitate to reach out to our support team via live chat. 🚀