Detailed feature comparison
Compares various graph libraries including igraph, Boost, Networkit, Snap, Graph-tool, and Sage based on features and performance metrics. The first tab outlines the number of routines available in each library and highlights specific algorithms like Dijkstra and Floyd-Warshall shortest paths. The second tab presents performance data, detailing execution times for algorithms such as Diameter and Maximum flow across different input sizes. Notable performance insights include the execution time for Diameter in igraph at 47.076 seconds compared to 130.062 seconds in Boost. This comprehensive comparison serves as a valuable resource for developers and researchers in the field of graph analysis.
Content article
In this detailed feature comparison, various graph libraries are evaluated across numerous routines and functionalities, including igraph, Boost, Networkit, Snap, Graph-tool, and Sage, with a focus on the number of routines and specific capabilities.
The igraph library boasts 62 routines, while Boost has 31, Networkit has 28, and both Snap and Graph-tool each have 28 routines. Sage, before the project, has 66 routines, which increases to 98 after the project. Notably, Boost, Networkit, Snap, and Graph-tool have several routines that are not present in Sage, with Networkit having the highest count at 5, followed by Snap and Graph-tool with 2 and 5 respectively.
When examining distance calculations, the diameter is available in igraph, Boost, Snap, Graph-tool, and Sage, while Dijkstra's shortest paths are supported by all except Sage's Python version, which only has it in C. The Bellman-Ford algorithm is easy to implement in Sage, and Johnson's shortest paths are currently a work in progress. Floyd-Warshall is noted as available in Snap but missing in Sage.
In terms of graph traversal, breadth-first search (BFS) and depth-first search (DFS) routines are available across most libraries, although DFS is only in C for Sage. LexBFS is supported by igraph, Boost, and Networkit, while LexDFS is not specified. For minimum spanning trees, Kruskal's and Prim's algorithms are present in igraph, Boost, Networkit, and Snap.
The libraries also offer various flow and cut functionalities. All minimal separators are available in igraph, Boost, and Networkit, while the maximum flow/min cut is supported by all except Snap and Graph-tool, which only have it in C. Connected components are well-covered, with all libraries supporting the calculation of connected components, strongly connected components, and biconnected components, with vertex and edge connectivity routines present in several libraries.
Centrality measures are robustly represented, with betweenness centrality available in all libraries, and approximate versions present in igraph, Boost, Networkit, and Snap. Closeness centrality is also supported across the board, while eigenvector centrality is easy in Graph-tool and available in Sage. Pagerank is similarly easy in Graph-tool and available in Sage, with personalized pagerank only in igraph and Boost.
Diversity measures such as assortativity, bibcoupling, and cocitation are present in igraph, Boost, and Networkit. Local and global clustering coefficients are well-supported, with all libraries providing these measures. Eigenvalues and eigenvectors are available with Numpy in Sage, while tests for Eulerian and planar graphs are present in igraph, Boost, and Snap.
Turning to the performance comparison, the diameter calculation for undirected small graphs takes 47.076 seconds in igraph, 130.062 seconds in Boost, and a staggering 822.416 seconds in Snap, with Sage clocking in at 696.517 seconds. The maximum flow calculation is swift in igraph at 0.161 seconds, while Boost takes 6.041 seconds, and Networkit requires 1.595 seconds. Connected components are computed in 39.446 seconds in igraph, with Networkit performing impressively at 0.459 seconds.
For strong components in directed big graphs, igraph takes over 10,800 seconds, while Networkit is much faster at 0.876 seconds. Betweenness centrality is calculated in 4.502 seconds in igraph, whereas Snap takes 77.414 seconds. The clustering coefficient for undirected small graphs is 0.076 seconds in igraph and 4.01 seconds in Boost.
In terms of ratios with respect to the fastest library, the diameter in igraph is 1120.857, while Boost and Snap are significantly slower at 3096.714 and 19581.333 respectively. Maximum flow shows igraph as the fastest with a ratio of 1, while Boost is at 37.522 and Networkit at 9.907. Connected components yield a ratio of 85.939 for igraph, with Networkit at 2.298 and Snap at 18.699.
The performance metrics continue with strong components showing a ratio of 1.072 for Boost, while betweenness centrality has igraph at 1.806 and Snap at 31.053. The clustering coefficient shows igraph as the fastest with a ratio of 1, while Boost reaches 52.763 and Sage at 137.447. The results for clique number and preferential attachment also reflect significant performance disparities across the libraries, with igraph consistently demonstrating superior efficiency.