This is another library for creating and manipulating graphs. You can look at it two ways: first, igraph contains the implementation of quite a lot graph algorithms. These include classic graph algorithms like graph isomorphism, graph girth and connectivity and also the new wave graph algorithms like transitivity, graph motifs and community structure detection. Skim through the table of contents or the index of this book to get an impression.

Second, igraph provides a platform for the developing and/or implementing graph algorithms. It has a quite efficient data structure for representing graphs and a number of other data structures like flexible vectors, stacks, heaps, queues, adjacency lists to accomplish this. In fact these data structures evolved along the implementation of the classic and non-classic graph algorithms which make up the major part of the igraph library. This way they were fine tuned and checked for correctness several times.

Our main goal with developing igraph was to create a graph library which is efficient on large but not extremely large graphs. More precisely, it is assumed that the graph(s) fit into the physical memory of the computer. Nowadays this means graphs with several million vertices and/or edges. Our definition of efficient is that it runs fast, both in theory and (more importantly) in practice.

We believe that one of the big strengths of igraph is that it can be embedded into a higher level language or environment. Two such embeddings (or interfaces if you look at them the other way) are currently being developed by us: igraph as a GNU R package and igraph as a Python extension module. A third embedding, being developed by another developer is a Ruby extension. Other are likely to come. The high level languages as R or Python make it possible to do use graph routines with mush greater comfort, without actually writing a single line of C code. They have some, usually very small, speed penalty compared to the C version, but add ease and much flexibility. This manual however covers only the C library. If you want to use Python or GNU R, please see the documentation written specifically for these interfaces and come back here only if you're interested in some detail which is not covered in those documents.

We still consider igraph as a child project. It has much room for development and we are sure that it will improve a lot in the near future. Any feedback we can get from the users is very important for us, as most of the time these questions and comments guide us in what to add and what to improve.

igraph is open source and distributed under the terms of the GNU GPL. We strongly believe that all the algorithms used in science, let that be graph theory or not, should have an efficient open source implementation allowing use and modification for anyone.