orbital Network-based Frequency Analysis (oNFA)
orbital Network-based Frequency Analysis (oNFA) Heading link
The orbital Network-based Frequency Analysis (oNFA) tool advances the traditional NFA technique by assessing and comparing the performance of any entity part of a dataset to the main trend (identified as the network-based mode). To learn more about NFA, click here. oNFA calculate the “distance” of an entity to the main trend and plots it over time in polar coordinate, referred to as “orbital distance.” Moreover, the technique calculates the “orbital speed” of an entity by calculating how fast it converges to or diverges from the main trend over time.
The code can be downloaded by itself below or directly from GitHub. We have two main versions of the code that use two popular Python network libraries: networkx and igraph. Networkx is arguably more popular and easier to installed, but the igraph version is preferred because it runs much faster although it can be harder to install. A tutorial with a simple numerical example is posted below with the code. Please contact us if you have difficulty running the code.
For citation, please use Ahmad, N., Derrible, S., Managi, S., (2018) A Network-Based Frequency Approach to Representing the Inclusive Wealth of World Countries. Journal of Environmental Management, 218:348–354. doi: 10.1016/j.jenvman.2018.04.070, available here. See our publication page for full details.
version 1.20.igraph: GitHub | Download | Tutorial | Publication (for Python 2 and 3, using igraph – faster and preferred, but networkx version available if igraph cannot be installed)
version 1.20.networkx: GitHub | Download | Tutorial | Publication (for Python 2 and 3, using networkx – slower but easy to install, 1.20.igraph is preferred)
version 1.10: GitHub | Download | Tutorial | Publication (for Python 2 and 3)
version 1.00: GitHub | Download | Tutorial | Publication (some issues see later versions)
