Releases: abignoli/find-communities-OpenMP
Release 1.1
This release includes the definition of a framework for the development of solutions to
the problem of finding communities in large graphs.
This framework is intended to be a tool to study the problem, develop, implement and
analyze new solutions, both sequential and parallel. The solutions currently implemented are based
on the Louvain method, but there is room for expansion.
In this release, these algorithms are implemented:
- Sequential Louvain
- Parallel Sort & Select
- Parallel Naive Partitioning
- Parallel Sort & Select Chunk based
There are several execution options that allow the user to achieve an high degree of control,
besides the selection of the algorithm to run.
The implementation also easily allows to control the execution flow to create hybrids from
the existing algorithms and adapt in the best way to the input graph properties. This is,
however, left to the user's discretion and, as of now, no runtime tuning is implemented.
find-communities-framework-stable-v.01
This release includes the definition of a framework for the development of solutions to the problem of finding communities in large graphs. Currently the only implemented methods focus on the Louvain method, but there is room for expansion.
This framework is intended to be a tool to study the problem, develop, implement and analyze new solutions, both sequential and parallel.
In this release there are three different algorithms implemented.
- Sequential Louvain
- Parallel (Sort & Select)
- Parallel (Naive Partitioning)
The user is able to choose the preferred one at program start with the -a option. More information is contained in the help (that is shown using the -h option).
There are also options to control the number of threads, and several execution options.
The implementation also easily allow to control the flow execution to create hybrids from the existing solutions and adapt in the best way to the input graph properties. This is, however, left to the programmer discretion and, as of now, no runtime tuning is implemented.