Mapping and Understanding the Connections Between SIOP 2024 Conference Presenters

Here at Polinode we are incredibly excited about attending the SIOP Annual Conference for the first time. For those that may not have been before, the SIOP Annual Conference will be held in Chicago between the 17th and 20th of April and is the annual conference for the Society for Industrial and Organizational Psychology. It will see thousands of individuals (both researchers and practitioners), all of whom have an interest in I-O Psychology, come together for a few days to network and listen to hundreds of presentations on a wide variety of topics.

Given this will be our first SIOP conference, we thought it might be fun (and helpful for both ourselves and others) to use some publicly available data to create some networks that assist with understanding the conference (and provide some insight into the I-O Psychology community more generally). This blog post summarizes how we did this and includes some links to a few interactive networks so that you can explore them in more detail yourself.

Exploring a few largely untapped sources of data for passive Organizational Network Analysis

When it comes to Organizational Network Analysis (ONA) it’s common for organizations to reach for the traditional sources of data: they either run an Active ONA where they ask employees questions about their working relationships with others or they tap into the more common passive data sources such as metadata from emails, meeting invites and enterprise social networks such as Slack or Teams. At Polinode we are used to using this kind of data to help organizations understand how they are really functioning and also to improve in areas such as collaboration, DE&I, identifying talent and change management. But this is by no means all of the data that we work with. In fact, pretty much every organization (both large and small) has, in our experience, untapped sources of relationship data over and above these more common data sources. The purpose of this post is to highlight some of these less commonly utilized data sources.

Why is this important? Because often it can be easier to tap into these alternative data sources than, for example, email data. And these data sources can offer really valuable insights for what is often a relatively small effort.

Mapping the Unleash World Exhibitors Network Using LinkedIn Data

Tomorrow the Unleash World conference starts in Paris and we are incredibly excited! This will be the first time that Polinode has attended the Unleash World conference and both Andrew Pitts and Chad Taberner are very much looking forward to it after hearing amazing things.

Back in April of this year, we attended the Unleash America conference in Las Vegas. It was at that conference that we first had the idea of using the “People Also Viewed” data in LinkedIn to map the ecosystem of HR Tech companies based on a seed list. In this blog post after the event that is exactly what we did - used the list of sponsors for the Unleash America conference to map the HR Tech ecosystem.

Now, on the eve of the Unleash World conference, we thought it was only appropriate to extend the same analysis to the Unleash World conference. That is to say, to take the list of the ~175 exhibitors at the Unleash World conference and use them as a seed list to then create a network of relationships between these sponsors (and other companies) using the People Also Viewed data from Linkedin as pictured below for Polinode.

Using LinkedIn Data and Network Analysis to Uncover Additional HR Tech Influencers

This is an exciting week in the HR world with the HR Technology Conference taking place in Vegas. Chad Taberner and I (Andrew Pitts) will be there all week and are looking forward to seeing both new and familiar faces at the Polinode booth (#1433).

Inspired by the work we did a couple of months ago, where we used the People Also Viewed data from LinkedIn to map the global People Analytics network, we decided to create a network based on the 2023 Top 100 HR Tech Influencers. This list is published each year by the editorial team at Human Resource Executive and the organizers of the HR Technology Conference.

The first step in the process was to extract the 100 LinkedIn profiles of the 2023 Top 100 HR Tech Influencers. We then retrieved the list of “People Also Viewed” profiles for each of these individuals from LinkedIn. You can see an example of these People Also Viewed profiles for my own profile below.

Mapping the Global People Analytics Network Using LinkedIn Data

In our last blog post we took a look at a network map of companies in the HR Tech space. For that we used the “People Also Viewed” data for LinkedIn company profiles. After completing that analysis we had another idea - why don’t we use the same People Also Viewed but instead of for company profiles rather extract it from the profiles of individuals on LinkedIn? And that is exactly what we have done and summarized in this post.

As a company that focuses on the People Analytics space, one of the most interesting networks for us is the global People Analytics network, i.e. the network that helps understand the relationships and connections that exist between people who work in the People Analytics space globally. This post summarizes the approach we took to mapping this network using LinkedIn data and also walks through some of the key insights from this network. It’s not designed to be a comprehensive analysis of the network though and we are sure that others in this space will extract other insights. It’s for this reason that we’ve also provided links to the interactive networks in Polinode below.

Using LinkedIn Data to Help Understand the HR Tech Ecosystem

In April we attended the Unleash America Conference in Las Vegas. Altogether it was a great event and we had a lot of interesting conversations and made a lot of great connections. While we were at the conference we started to think about the connections that exist between the exhibitors at the conference. This post is the result of some analysis we did as a result and that we are now pleased to share publicly. As I’m sure you will see, there are a great deal of insights that one can glean from the approach we have taken and we have not attempted an exhaustive analysis here but rather have made the resulting networks publicly available below so that you can access them and ask your own questions.

Announcing an Integration with Google Workspace

Nine months ago we announced an integration between Polinode and Office 365 which enabled our Enterprise users to easily (i.e. in less than five minutes) plug into Office 365 email and calendar data in order to then visualize and analyze that data as networks in Polinode. Today we are pleased to announce that we are bringing the same functionality to Google Workspace (previously G Suite)! For the first time it’s now possible to easily extract metadata from Google Workspace Email and Calendar data and use that data for powerful Organizational Network Analysis.

Introducing an Integrated Tool to Identify Influencers and Dynamic Collaboration Matrices

We are excited to announce that two new powerful features have just gone live! A handful of users have been testing these features over the last few weeks and the feedback has been very positive indeed so we wanted to share them with the broader community as soon as possible. In summary, these new features are:

  1. Identify Influencers: We have added the ability to identify those individuals or nodes in the network that together maximise the coverage over the entire the network.

  2. Collaboration Matrices: We have added a new type of report called a Collaboration Matrix that summarises the interaction between different groups in a network.

New Features: New Metrics, Improved Labels, Simplified API and More

We really appreciate all the terrific suggestions for new features and improvements that have been provided recently! Today we are announcing that a number of new features have recently gone live: three new metrics, improved visual appearance of labels, a new simplified version of our API, the ability to save node positions when saving survey views and the ability to isolate a nodes edges by type. Below we describe each one of these five improvements.

Announcing an Integration Between Polinode and NodeXL Pro

Today we are thrilled to jointly announce with the Social Media Research Foundation,
developers of NodeXL, that NodeXL Pro now directly exports to Polinode Networks. This is a
very powerful integration and one that many users have asked for over the last few years. If you
haven’t come across NodeXL before, it’s a popular Excel add-in that supports social network
analysis. NodeXL Pro is the paid version of NodeXL and supports the collection of data from
social networks such as Facebook, Twitter and YouTube.


The bottom line is that by using this direct integration you can now collect social network data
directly from these social networks without writing a line of code and then export that data
directly into Polinode. Polinode is a browser-based tool for visualising and analysing network
data - one of the key attractions of using Polinode is that, because it’s browser-based, it’s easy
to share and collaborate on the analysis of social network data.