software_callgraphs — Software class collaboration and call graphs (2002)
- Description
Six networks of collaboration among software units in open-source software systems, parsed from their source code by C. R. Myers. Three are static class collaboration graphs of object-oriented systems written in C++, where each node is a class, and a directed edge B → A indicates that class B makes reference to class A in its definition, either through inheritance (B is a subclass of A) or aggregation (B holds an instance of A). The other three are static call graphs of procedural systems written in C, where each node is a function (subroutine), and a directed edge g → f indicates that g calls f from within its scope. Self-loops correspond to self-references and recursive calls.
vtk: class collaboration graph of version 4.0 of the VTK visualization library.dm: class collaboration graph of Digital Material, a library for atomistic simulation of materials (CVS snapshot of 3 April 2002).abiword: class collaboration graph of version 1.0.2 of the AbiWord word processor.linux: call graph of version 2.4.19 of the Linux kernel.mysql: call graph of version 3.23.32 of the MySQL relational database system.xmms: call graph of version 1.2.7 of the XMMS multimedia player.
The networks have the following properties:
- Nodes:
name, the name of the class or function.
- Tags
- Technological Software Unweighted Metadata
- Citation
-
- C. R. Myers, "Software systems as complex networks: Structure, function, and evolvability of software collaboration graphs." Physical Review E 68, 046116 (2003) 10.1103/PhysRevE.68.046116 Sci-Hub
- Upstream URL OK
- https://biohpc.cornell.edu/staff/myers/Data/SoftwareGraphs/
- Networks
-
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv vtk 788 1,389 1.76 5.73 8.87 29.44 -0.19 0.04 11 0.98 Directed Unipartite name 8 KiB 13 KiB 12 KiB 11 KiB dm 187 278 1.49 3.95 6.41 33.35 -0.13 0.08 10 0.87 Directed Unipartite name 3 KiB 4 KiB 3 KiB 3 KiB abiword 1,096 1,857 1.69 5.05 9.13 202.92 -0.06 0.04 12 0.94 Directed Unipartite name 11 KiB 16 KiB 16 KiB 14 KiB linux 5,420 11,460 2.11 15.67 16.82 109.12 -0.07 0.01 17 0.98 Directed Unipartite name 60 KiB 90 KiB 91 KiB 86 KiB mysql 1,501 4,245 2.83 9.91 18.20 1154.46 -0.08 0.06 16 0.99 Directed Unipartite name 18 KiB 29 KiB 28 KiB 25 KiB xmms 1,097 1,901 1.73 4.00 8.53 183.52 -0.08 0.04 17 0.89 Directed Unipartite name 12 KiB 17 KiB 17 KiB 16 KiB