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TRAC: Influencer Network Analysis

Discover how to use Pulsar's Influencer Network Graph to identify highly-connected individuals and understand their influence.

Updated over a year ago

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 interest 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. Twitter, Tumblr, Instagram, Blogs, Forums, Reviews, News). 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 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.

So, how can you use the "Influencer Network Graph" to gain insights into highly-connected individuals and their influence around a specific topic?

Understanding the graph is straightforward. Each node (circle) represents an author and the size of each node represents the number of engagements that the author has generated. The lines connecting the nodes are called edges, and we shorten or lengthen the edges based on the weight of the specific node (i.e.) the number of engagements it has generated. To identify highly-connected individuals, you need to look for nodes that have a high number of connections, which are typically located at the centre of the clusters. By focusing on these groups within the network, you can gain insights into how the discussion flows from one person to another and identify communication patterns around the online conversations that you're analysing.

The more engagements an author generates, the greater their influence within the network, which can provide valuable insights for marketers and researchers looking to understand the dynamics of online communities and conversations.

💡 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.

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! 📚

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