# Frequently asked questions

## I tried to install igraph but got an error! What do I do?

First, look at our installation instructions including the troubleshooting section. If that does not solve your problem, reach out via the igraph forum. We’ll try our best to help you!

## I’ve just installed igraph. What do I do now?

Take a peek at the Quick Start! You can then go through a few more examples in our gallery, read detailed instructions on graph generation, analysis and visualisation, and check out the full API documentation.

## I thought igraph was an R package, is this the same package?

igraph is a software library written in C with interfaces in various programming languages such as R, Python, and Mathematica. Many functions will have similar names and functionality across languages, but the matching is not perfect, so you will occasionally find functions that are supported in one language but not another. See the FAQ below for instructions about how to request a feature.

## I would like to use igraph but don’t know Python, what to do?

igraph can be used from multiple programming languages such as C, R, Python, and Mathematica. While the exact function names differ a bit, most functionality is shared, so if you can code any of them you can use igraph: just refer to the installation instructions for the appropriate language on our homepage.

If you are not familiar with programming at all, or if you don’t know any Python but would still like to use the Python interface for igraph, you should start by learning Python first. There are many resources online including online classes, videos, tutorials, etc. igraph does not use a lot of advanced Python-specific tricks, so once you can use a standard module such as `pandas` or `matplotlib`, igraph should be easy to pick up.

## I would like to have a function for operation/algorithm X, can you add it?

We are continuously extending igraph to include new functionality, and requests from our community are the best way to guide those efforts. Of course, we are just a few folks so we cannot guarantee that each and every obscure community detection algorithm will be included in the package. Please open a new thread on our forum describing your request. If your request is to adapt an existing function or specific piece of code, you can directly open a GitHub issue (make sure a similar issue does not exist yet! - If it does, comment there instead.)

## What’s the difference between igraph and similar packages (networkx, graph-tool)?

All those packages focus on graph/network analysis.

Warning

The following differences and similarities are considered correct as of the time of writing (Jan 2022). If you identify incorrect or outdated information, please open a Github issue and we’ll update it.

Differences:

> - igraph supports multiple programming languages (e.g. C, Python, R, Mathematica). networkx and graph-tool are Python only.

> - igraph’s core library is written in C, which makes it often faster than networkx. graph-tool is written in heavily templated C++, so it can be as fast as igraph but supports fewer architectures. Compiling graph-tool can take much longer than igraph (hours versus around a minute).

> - igraph vertices are ordered with contiguous numerical IDs, from 0 upwards, and an optional “vertex name”. networkx nodes are defined by their name and not ordered.

> - Same holds for edges, ordered with integer IDs in igraph, not so in networkx.

> - igraph can plot graphs using `matplotlib` and has experimental support for plotly, so it can produce animations, notebook widgets, and interactive plots (e.g. zoom, panning). networkx has excellent `matplotlib` support but no plotly support. graph-tool only supports static images via Cairo and GTK+.

> - In terms of design, igraph really shines when you have a relatively static network that you want to analyse, while it can struggle with very dynamic networks that gain and lose vertices and edges all the time. This might change in the near future as we improve igraph’s core C library. At the moment, networkx is probably better suited for simulating such highly dynamic graphs.

Similarities:

> - Many tasks can be achieved equally well with igraph, graph-tool, and networkx.

> - All can read and write a number of graph file formats.

> - All can visualize graphs, with different strengths and weaknesses.

Note

igraph includes conversion functions from/to networkx, so you can create and manipulate a network with igraph and later on convert it to networkx or graph-tool if you need. Vice versa, you can load a graph in networkx or graph-tool and convert the graph into an igraph object if you need more speed, a specific algorithm, matplotlib animations, etc. You can even use igraph to convert graphs from networkx to graph-tool and vice versa!

## I would like to contribute to igraph, where do I start?

Thank you for your enthusiasm! igraph is a great opportunity to give back to the open source community or just learn about graphs. Depending on your skills in software engineering, programming, communication, or data science some tasks might be better suited than others.

If you want to code straight away, take a look at the GitHub issues and see if you find one that sounds easy enough and sparks your interest, and write a message saying you’re interested in taking it on. We’ll reply ASAP and guide you as of your next steps.

The C core library also has various “theory issues”. You can contribute to these issues without any programming knowledge by researching graph literature or finding the solution to a graph problem. Once the theory obstacle has been overcome, others can move on to the coding part: a real team effort!

If none of those look feasible, or if you have a specific idea, or still if you would like to contribute in other ways than pure programming, reach out on our forum and we’ll come up with some ideas.