TU Berlin

Algorithmics and Computational Complexity Research GroupSoftware


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Our software
short description
A tool for analyzing the combinatorial structure of graphs by computation and estimation of various graph parameters.
Incremental Graph Coloring
An implementation of serveral commonly used heuristic algorithms for the (vertex) graph coloring problem together with a new proposed search tree based algorithm that is based on "incremental coloring".

Tool for computing Kemeny rankings by using efficient polynomial-time data reduction rules combined with several exact algorithms. Can solve meaningful real-world instances with more than 100 candidates within milliseconds.
Tool for computing maximum mu-cliques.
Tool for finding maximum s-plexes.
Temporal cliques
Tool for enumerating all maximal cliques in a temporal graph
Pattern-Guided k-Anonymity
Heuristic and exact ILP implementation to solve Pattern-Guided k-Anonymity. The collection includes the scripts and datasets used in the paper.
Heuristic and exact ILP implementation including the random instance generator. Binaries are created on 64bit Debian.
Implementations of both the naive and the cluster boundary based SAT-Solving approach.
Parliamentary voting procedures
Implementations for both Manipulation and Agenda Control for the amendment procedure and the successive procedure.
Highly Connected Deletion
This package contains the source and the test data for a solver for
the Highly Connected Deletion problem.
Well-connected 2-club
Tool for finding well-connected 2-clubs. The implementation can find the support the t-robust, t-hereditary,  and the t-connected 2-club model.
Implementation of a Dynamic Program computing a mean of time series in dynamic time warping spaces.
Maximum Matching
Implementations of both weighted and unweighted reduction rules for maximum matching.
Dynamic Temporal Graph Warping
Implementation of the dtgw-distance algorithms.
Isolated Temporal Cliques
Enumeration algorithm for isolated temporal cliques.
Cluster Editing
Java-Implementation for transforming a given input graph into a cluster graph (disjoint union of cliques) with a minimum number of edge modifications.
Persistent graphs
Enumeration of persistent graphs.
Binary DTW-Mean
Implementation of fast DTW-Mean algorithms for binary data.
Implementation of fast exact DTW for run-length encoded time series.


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