A close look at the Gephi user community

Last December we asked Gephi users to participate in a survey. The survey’s main objective was to better understand who users are and what kind of projects they work on. One important dimension we wanted to explore was the diversity of the user community. Through the projects we’ve seen in research and on the web we knew that Gephi users were diverse, but we wanted to quantify it. Ultimately, we aim to make the tool better so it supports users’ needs, but this is a process that requires first a good understanding of who the audience is and what are their objectives. Below we summarized our findings about the profile of users, the types of networks they work with and finally useful usage statistics the community can reflect on.

Profile

The largest share of Gephi users work in academia. The project started in the academic sphere from where it has spread into business, artistic and non-profits domains as well. Working at a profit organization is the second most common occupation, which confirms that network analysis is no longer reserved to scientists.

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Q12. What is your occupation? n=285; multiple choice

Given that the largest group of users works in academia, it is not surprising that the most common title among Gephi users is a researcher.

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Q14. What is your title? n=285; multiple choice

The user community is also widely spread around the world. Users from 46 different countries participated in this study. This confirms the importance of localization for as many languages as possible (Gephi currently supports eight). While many countries were represented by only a handful number of participants in the study, large concentration of users is, as expected, in the US (23%) and in France (15%). Significant presence in France is predetermined by Gephi’s presence in universities and businesses within which Gephi was originally founded.

Networks

Social networks are by far the most commonly analyzed type of networks when using Gephi. 70% say that they typically analyze social networks when using Gephi. Social media and semantic network analysis are also common and typically analyzed by 46% and 43%, respectively. The rest of the networks are less common with ecological network analyzed by about 5% of users.

Despite SNA (Social Network Analysis) being the dominant use there is a large variety of other use as well. That said, networks can be analyzed only if the data are accessible and we (the community) still have work to do to ease network collection and formatting.

We always wondered if given occupations are more likely to work with specific types of networks. Based on this study, some differences exist, but they are not as prominent as we have expected. We found that people working at profit organizations are more likely to use Gephi to analyze business and financial networks. While in total 24% use Gephi to analyze business network, it is 44% among those who work in a profit organization compared to only 12% among those who do not work in a profit company. Differences for other types of networks were not conclusive.

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Q5. What type(s) of network do you typically analyze using Gephi? n=285; multiple choice

Gephi users commonly deal with a wide range of network sizes. Although the typical network has between 100 to 10K nodes, every size from <100 nodes to 1M nodes represent at least 10% of users. In total that is more than 5 orders of magnitude difference in data size, and without taking edges in consideration!

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Q6. What is/are the graph size(s) you deal with when working with Gephi? n=285; multiple choice
Q7. And what is the TYPICAL size of a graph that you manipulate with Gephi? n=285; single choice

While more than half of Gephi users have never used Gephi to analyse dynamic networks, the vast majority of the community is likely to use it in the future.  This confirms the importance of the set of features related to dynamic networks that has long been one of Gephi’s primary focus.

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Q8. Have you ever used Gephi to work with dynamic networks (networks over time)? n=285; single choice

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Q9. How likely are you to use Gephi to analyze dynamic networks (networks over time) in the future? n=285; single choice

Usage

Both online and offline sources are important touch points through which people learn about Gephi for the first time. While web search is the most common way how people find Gephi, word of mouth remains an important channel and is not to be underestimated.

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Q2. How did you first learn about Gephi? n=285, single choice

The community is very diverse when it comes to usage frequency which suggests that Gephi users are likely to have diverse needs. Occasional users are likely to have different expectations from a software than regular users.  About one third uses Gephi at least once a week which confirms that there is a relatively large base of heavy users who use Gephi regularly.

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Q3. On average, how often do you use Gephi? n=285; single choice

Online tutorials and online forums are key sources for users to learn about Gephi. This confirms the importance of creating and updating online tutorials. It also suggests that the community is well engaged to be able to provide answers one another on online forums and groups.

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Q4. What source(s) have you used/are you using to learn how to use Gephi? n=285, multiple choice

Conclusion

This survey is a first, yet important step in understanding the Gephi user community at large. It also gives a general overview of the network visualization and analytics field and we hope this can be useful for others as well. But for us – the Gephi leadership team – this will help us in our future community management efforts. It will also help design a better tool in the future as we better understand its user community.

In addition, talking about what kinds of projects users work on also helps shape the understanding of what network analytics is used for, and ultimately bring more people to the community. In the near future we want to double-down on this topic and start a series of articles highlighting the most interesting projects. Many of the respondents indicated their willingness to share what they have worked on so there’s already plenty to choose from.

Finally, to reflect on the diversity of users we believe it simply reflects that networks are everywhere. Analyzing networks bring insights and answers to many different problems.

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Appendix
  • Survey was conducted among Gephi users community. While the results provide a unique view into the Gephi community it is important to clarify that they are not meant to be representative of the entire community world wide.
  • The survey invitations were distributed throughout the week of Dec 1st 2015 via email, Twitter and Facebook
  • Final data set contains responses collected between Dec 1st 2015 and Dec 23rd 2015
  • A total of 285 participants completed the survey

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