Visualizing HF Networks


A flight of G-VFIT from Atlanta to Heathrow, and its HFDL communications – visualized by GEPHI.

Gephi does it

Each communications has a structure. Visualization reveals this structure. This is also valid for HF communications with its networks with different stations, (even moving) locations, hierarchy …

Recently, I made my first steps with free visualization software GEPHI to get a deeper look into some aeronautical networks. The graph at the top shows the gephi’ed result of 125+ HFDL messages, transmitted by a flight of G-VFIT from Atlanta to Heathrow. I monitored six HFDL channels in parallel, one channel from New York, two from Shannon, and three from Reykjavik.

Each point represents a message, tagged by its time in UTC. The positions of the points are geo-referenced, as I used HFDL messages containing these information.

Wheras the longitude’s positions are to scale, their latitude’s positions had been spread for better reading.

This visualization shows that Reykjavik on 6.712 kHz did the main work. But it is surprising that the first contact just leaving the U.S. coast was made with Shannon, and not with nearby New York.

GEPHI also helps in visualizing the hiearchy of networks, see screenshot below:

In the Russian Aero Net on 4.712 kHz, Rostov plays a pivotal role.

Here the strength of the connecting lines and the position of the city’s names represents to hierarchy of this network, i.e. who is calling whom, and how often. There are several strongholds like Rostov, Yekaterinburg and Samara, but also some mere outposts like Novosibirsk and Syktyvkar.

This picture isn’t geo-, but social-referenced, to say so: you know that e.g. Chelyabinsk on the left is geographically situated east of Rostov. You may also geo-reference these data, turn it into a kml file and see it in Google Maps of Open Street Map. If the co-ordinates are correct, zooming will take you exactly to the feeding point of each of their Nadenenko dipole, see below …

The above visualized hierarchy has been changed here into a geo-referenced kml file, opened in Google Earth.

There are many more applications of such a great tool for visualization which will further enrich monitoring.


  • Brilliant stuff, Nils, I admire your work and those who develop the analytical/presentation tools.

  • Nice work , Nils! We used Gephi some time ago in a social net work analysis course, never thought of using it in this context, brilliant! 🙂

    • … thanks, Marc. And as cooking is my other hobby, I always look for any ingredients to make meal also for DXing 😉 What attracted me most at GEPHI is there geo-spatial feature. Commercial software will give even nicer visualizations, but at a hefty price. 73 Nils, DK8OK

  • It has long been a part of Krypto500

  • … right, Sir! Although I have tested Krypto500 many years ago, I wasn’t able to take a test drive of the recent version. And it doesn’t look that promising … I like to write primarly about things, I know from own eyesight. So I would be very interested in how they implement GEPHI. They have, AFAIK, just a simple star network as example on their website. So – do they also use the geo-spatial potential of GEPHI plus this in combination with time and frequency, as shown in my example with the trans-atlantic flight? 73 Nils, DK8OK

  • Thanks, Pierre – and if I remember correctly, the basis was a spreadsheet file – then into GEPHI, into klm. Instead of Google, I would like prefer Tableau Public, nowadays … 73 Nils, DK8OK

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