NetworKit

Large-Scale Network Analysis

- New feature
Windows 7, 8.1 and 10: possibility to install NetworKit via pip. Currently we have no pre-built wheel-package available so you have to make sure that the MSVC-compiler (cl.exe) can be found when installing NetworKit via pip. A possible solution is to call “pip” from within “Native Tools Command Prompt” provided by Visual Studio. This feature will be further improved in the future.

New algorithms

- Centrality:
Greedy algorithm for group harmonic closeness based on “Group-Harmonic and Group-Closeness Maximization - Approximation and Engineering”, E. Angriman, R. Becker, G. D’Angelo, H. Gilbert, A. van der Grinten, H. Meyerhenke, ALENEX 2021. This algorithm is available in

`networkit.centrality.GroupHarmonicCloseness`

.Local search approximation algorithm for group closeness based on the aforementioned ALENEX 2021 paper. This algorithm is available in

`networkit.centrality.GroupClosenessLocalSearch`

.Heuristic algorithm for group closeness (LS-Restrict) based on “Local Search for Group Closeness Maximization on Big Graphs”, E.Angriman, A. van der Grinten, H. Meyerhenke, IEEE BigData 2019. This algorithm is available in

`networkit.centrality.GroupClosenessLocalSwaps`

.New algorithm for Normalized PageRank based on “Comparing Apples and Oranges: Normalized PageRank for Evolving Graphs”, K. Berberich, S. Bedathur, G. Weikum, M. Vazirgiannis, WWW 2007. The algorithm is available in

`networkit.centrality.PageRank`

.

- Community Detection:
Based on Map Equation, available via

`networkit.community.LouvainMapEquation`

. For further information about the algorithm, see “The map equation”, M. Rosvall, D. Axelsson, C. T. Bergstrom, EPJ ST 2009.Based on Overlapping Normalized Mutual Information, from the paper “Normalized Mutual Information to Evaluate Overlapping”, A. McDaid, D. Greene, N. Hurley, Physics and Society 2011. This algorithm is available in

`networkit.community.OverlappingNMIDistance`

.

- Matching:
Suitor matcher, based on “New Effective Multithreaded Matching Algorithms”, F. Manne and M. Halappanavar, IPDPS 2014. This algorithm is available in

`networkit.matching.SuitorMatcher`

.

- GraphTools:
New function

`subgraphFromNodes`

: returns an induced subgraph based on an input graphThe previous

`subgraphFromNodes`

has been renamed to`subgraphAndNeighborsFromNodes`

in order to better reflect its functionality

- Further changes and improvements
Template implementation of CSRMatrix

Clang-analyzer warnings are fixed and treated as errors

Improved performance of graph writers

Possibility to try-out NetworKit without installation: binder support + cloud instances

Optimized memory usage in LAMG and ConjugateGradient

Improved runtime of (parallel) coarsening implementation for clusterings

Improved runtime of isProper() for matching

Support for clang-12 and gcc-11 compilers

AVX2 support for Windows

New features

New embedding module that implements the node2vec algorithm based on “node2vec: Scalable feature learning for networks” by Grover and Leskovec (KDD 2016). The embedding module is available for both C++ and Python.

New csbridge Python module that allows to draw colored graphs inline in a jupyter notebook via ipycytoscape.

Better implementation of

`ClusterRandomGraphGenerator`

: now it takes linear time and supports parallelism.Added support for Binder. Newer branches from NetworKit can now be accessed directly from Binder. Currently supported are master (newest stable) and 8.1 (release version).

For developers

We raised the minimum required clang version from 3.8 to 3.9.

It is now possible to create the Python package against an external pre-build tlx-library. To use it, add

`--external-tlx=<TLX_PATH>`

to`setup.py build_ext-phase`

.All clang-tidy warnings have been resolved and will be treated as errors by our CI pipeline. Some of the clang-tidy checks also involve possible performance enhancements and/or lowering of the memory footprint by avoiding unnecessary copies. The exact benefit depends on the use-case.

Several warning and documentation fixes.

Notable bugfixes

When using custom compilers on macOS (for example homebrew gcc compiler) and NetworKit was built from source with an external core, this created a NetworKit installation with incompatible core and cython-extension libraries.

In

`KatzCentrality`

, the parameter alpha was set to 0 by default. This caused the edges to be ignored and every node got the same centrality.

The paper “New Approximation Algorithms for Forest Closeness Centrality - for Individual Vertices and Vertex Groups” (authors: van der Grinten, Angriman, Predari, Meyerhenke) was selected for publication by SIAM Data Mining 2021. In the paper NetworKit is used for computing the experimental data. We also plan to include the new Forest Closeness Centrality algorithms in future releases.

New features

Possibility to specify edge directions for Katz centrality

New algorithm to approximate Electrical Closeness, based on Approximation of the Diagonal of a Laplacian’s Pseudoinverse for Complex Network Analysis by E. Angriman, A. van der Grinten, M. Predari and H. Meyerhenke

New algorithm: SPSP (Some Pairs Shortest Paths), as APSP but with user-specified source vertices

New features for Contributors / Developers

We moved our continious integration testing from Travis-CI to Github Actions. While the test-coverage stays the same, testing time is significantly reduced. This results in faster feedback for your pull requests.

Based on our rule to support compilers which are 5 years old, the minimum support for gcc was raised to version 5.

NetworKit now support C++14 features.

Further Improvements

The documentation is improved and includes rendering-fixes, when dealing with certain elements like formulas.

Refactored

`Betweenness`

and`ApproxBetweenness`

, leading to improved parallel performance.

New features for Contributors / Developers

We restructured the Cython-Interface (responsible for the connection between Python and C++ core-libraries) in order to make development and maintenance more approachable. As a result the previous monolithic file

`_NetworKit.pyx`

is now split into modules, resembling the structure of the C++ code. New modules can be added easily by providing appropriate Cython-files in sub-folder networkit.

Further Improvements

Refactored the EdgeListReader, leading to a speed-up when reading in edge-list based graph files.

Additional Notes

Beginning with release

`7.1`

(`7.0`

also available) NetworKit is now also distributed via package managers conda, spack and brew. All channels provide different packages for the C++ headers/library and the complete Python/C++ software. Head over to github for installation instructions.

New Features

New algorithms for GedWalk centrality based on the paper Group Centrality Maximization for Large-scale Graphs (ALENEX 2020).

New parallel implementation of the Hayashi et al. algorithm for spanning edge centrality approximation.

PageRank: possibility to choose between the L1 and the L2 norms as stopping criterion of the algorithm, and to set a maximum number of iterations.

GlobalThresholdFilter: support for weighted and directed graphs.

Notable Bugfixes

CommuteTimeDistance now returns the correct distance between two nodes for computation with and without preprocessing

Fix of an error in the

`exportGraph`

-function of GephiStreamingFix of an error in APSP that returned wrong distances in disconnected graphs

Further Improvements

Support for newer Python-version: 3.8

Support for newer compiler: gcc 10.1, AppleClang 11.03

Reduce memory footprint of several functions/classes: BFS, Dijkstra, SSSP, TopCloseness

Reduce time-complexity of several functions/classes: GephiStreamer, StaticDegreeSequenceGenerator, TopCloseness, WattsStrogatzGenerator

Added more notebook as examples

Additional Notes for Contributors Developers

Development will be done on the master branch, the Dev branch will not be used anymore.

“Scaling up Network Centrality Computations - a Brief Overview” was accepted for publishing in the journal it - Information Technology.

“Scaling Betweenness Approximation to Billions of Edges by MPI-based Adaptive Sampling” accepted for IPDPS 2020.

In the following you see an overview about the contributions, which went into NetworKit 6.1.0. Note that this version is fully compatible with release 6.0.0.

New features

Introducing new iterators for nodes and edges to have a coherent, idiomatic and fast way to repeate tasks for different elements of a graph. Syntax-wise the iterators can be called similarly in Python and C++. In Python iterating can be invoked by

`for x in graph.iterNodes()`

, whereas the counter-part for C++ works with`for(node x: graph.nodeRange())`

. Internally, all functions in NetworKit already use the new iterators.cmake adds more options to support variants of clang-compilers with OpenMP for macOS and Linux. This includes conda, homebrew and MacPort-environments.

Bugfixes

Generating a graph with the Watts-Strogatz algorithm does not lead anymore to infinite loops, when passing a number of neighbors per node, which is equal to the total number of nodes in the graph. (See issue #505)

Fixed error in function inNeighbors, including not all parameters in call to underlying library. (See issue #469)

The z-coordinate is now correctly scaled when writing a graph to GML. (See issue #500)

ConnectedComponents::extractLargestConnectedComponent now returns a compacted graph if called with appropriate parameters.

Deprecated features

Nested-parallelism-feature is now marked as deprecated.

Patch notes

Added an option to cmake (-DNETWORKIT_EXT_TLX), which enables to link against an externally built tlx-library

Updated travis-configuration in order to remove deprecated options

Fixed a bug, which prevented the headers from ttmath to be installed correctly

New features

NetworKit binary graphs: new binary graph format that is both smaller usually smaller than text-based formats and also faster to read. The format allows for parallel reading. It supports (un-)directed as well as (un-)weighted graphs and deleted nodes.

KadabraBetweenness: implementation of a new parallel algorithm for betweenness approximation. This is based on the definition from “Parallel Adaptive Sampling with almost no Synchronization”, A. van der Grinten, E. Angriman, H. Meyerhenke

New method in ConnetedComponents to extract the largest connected component of a given graph.

BidirectionalBFS and BidirectionalDijkstra: new algorithms for faster graph exploration when the target vertex is known.

New method in Graph to remove all duplicate edges (i.e. additional edges with same source and same target as another edge).

New notebooks with tutorials for Centrality, Community detection, Components, Distance, Generators, Graph, Graph read/write, Randomization.

Removal of deprecated features (see list below for more informations)

New release cycle and version numbering: NetworKit now releases a major release every half a year, and an optional minor release every quarter. See you in summer 2020 for NetworKit 7.0 then.

Package Manager support: conda, spack, brew and more packages will be created starting with 6.0. They will follow the github/PyPI-release in the coming weeks.

New features for developers

Clang format: new .clang-format configuration file to format NetworKit C++ files.

Header files: all C++ header files have been moved to the include/ directory.

Notable bugfixes

“make install” and “ninja install” now correctly install the NetworKIt C++ library together with its header files. The pkg-config utility is supported to link against the library.

NetworKit now always logs to stderr instead of stdout (regardless of the log level). This change makes life easier for programs that link against NetworKit as a library but also need to adhere to a specific output format on stdout.

ApproxGroupBetweenness now uses much less memory and can scale to larger graphs.

Deprecated features

The following Graph methods have been deprecated: getId, typ, setName, getName, toString, nodes, edges, neighbors, time, timeStep.

The following Graph methods have been deprecated and moved to GraphTools: copyNodes, subgraphFromNodes, transpose, BFSfrom, DFSfrom. toUnweighted, toUndirected, append, merge, volume

A deprecated constructor of the KONECTGraphReader class has been removed.

The deprecated FrutchermanReingold, and MultilevelLayouter algorithms have been removed.

The deprecated MaxClique algorithm has been removed.

The deprecated SSSP::getStack() method has been removed.

The following deprecated methods in Graph have been removed: addNode(float, float), setCoordinate, getCoordinate, minCoordinate, maxCoordinate, initCoordinate

“Local Search for Group Closeness Maximization on Big Graphs”, accepted for IEEE BigData 2019.

“Group Centrality Maximization for Large-scale Graphs” accepted for ALENEX 2020.

“Guidelines for Experimental Algorithmics: A Case Study in Network Analysis” was accepted and published by the open-access journal

*Algorithms*. It is part of the Special Issue: “Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial” edited by Daniele Frigioni and Mattia D’Emidio. More information can be found here: https://www.mdpi.com/1999-4893/12/7/127.“Parallel Adaptive Sampling with almost no Synchronization” accepted for Euro-Par 2019.

“Scalable Katz Ranking Computation in Large Static and Dynamic Graphs” accepted for Esa 2018.

“Parallel and I/O-efficient Randomisation of Massive Networks using Global Curveball Trades” accepted for Esa 2018.

“The Polynomial Volume Law of Complex Networks in the Context of Local and Global Optimization” in Scientific Reports.

“Computing Top-k Closeness Centrality in Fully-dynamic Graphs” accepted for ALENEX 2018.

Major features:

New algorithm for approximating of the betweenness centrality of all the nodes of a graph or of the top-k nodes with highest betweenness centrality based on: “KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation”, M. Borassi, E. Natale. Presented at ESA 2016.

New Mocnik graph generator based on: “Modelling Spatial Structures”, F.B. Mocnik, A. Frank. Presented at COSIT 2015.

New build system based on CMake.

Support for C++ build on Windows.

Minor changes:

Parallel Erdos Reny graph generator.

NetworKit installation via pip: missing packages will be automatically downloaded.

Partition: equality between partitions can be quickly checked via hashing.

Closeness: generalized definition of Closeness centrality so it can be computed also on disconnected graphs.

Aux::PrioQueue allows read access to its elements via iterators.

Graph class: new reductions allow to compute the maximum (weighted) degree of a graph in parallel.

Today we announce the next version of NetworKit, the open-source toolkit for large-scale network analysis. NetworKit is a Python package, with performance-critical algorithms implemented in C++/OpenMP.

**Release notes**

Major features:

Dynamic algorithm for keeping track of k nodes with highest closeness centrality (based on “Computing Top-k Closeness Centrality in Fully-dynamic Graphs”, P. Bisenius, E. Bergamini, E. Angriman and H. Meyerhenke. Presented at ALENEX 2018).

Dynamic algorithm to keep track of k nodes with highest Katz centrality (based on “Scalable Katz Ranking Computation in Large Static and Dynamic Graphs”, A. van der Grinten, E. Bergamini, O. Green, D. A. Bader and H. Meyerhenke.).

Curveball graph randomization algorithm based on “Parallel and I/O-efficient Randomisation of Massive Networks using Global Curveball Trades”, C. J. Carstens, M. Hamann, U. Meyer, M. Penschuck, H. Tran and D. Wagner.

Algorithm for finding the group of nodes with highest betweenness centrality (based on “Scalable Betweenness Centrality Maximization via Sampling”, A. Mahmoody, C. E. Tsourakakis, E. Upfal).

Algorithm for finding the group of nodes with highest group degree based on the definition in “The Centrality of Groups and Classes”, M.G. Everett, S.P. Borgatti.

Algorithm for finding all the biconnected components of a graph based on “Algorithm 447: efficient algorithms for graph manipulation”, J. Hopcroft, R. Tarjan.

Support for binary graph I/O: Support for graphs exported by Thrill (see https://github.com/thrill/thrill), and Implementation of binary partition readers and writers that are potentially faster than their text-based counterparts.

Minor changes:

All algorithms for finding the top-k (harmonic) closeness can also return all the nodes whose centrality is equal to the k-th highest. This behaviour can be triggered by parameter passed in the constructor of the class.

Faster KONECT and SNAP graph readers: roughly 2x speedup on the previous readers.

Greatly improved running time of NetworKit’s unit tests.

Size reduction of the “input” folder. In case of space constraints, we suggest to do a shallow clone of the NetworKit repository: git clone –depth=1 http://github.com/networkit/networkit

Today we announce the next version of NetworKit, the open-source toolkit for large-scale network analysis. NetworKit is a Python package, with performance-critical algorithms implemented in C++/OpenMP.

**Release notes**

Major:

Algorithm for finding the group of nodes with highest closeness centrality (based on “Scaling up Group Closeness Maximization”, E. Bergamini, T. Gonser and H. Meyerhenke. To appear at ALENEX 2018).

Dynamic algorithm for updating the betweenness of a single node faster than updating it for all nodes (based on “Improving the betweenness centrality of a node by adding links”, E. Bergamini, P. Crescenzi, G. D’Angelo, H. Meyerhenke, L. Severini and Y. Velaj. Accepted by JEA).

Dynamic algorithm for keeping track of k nodes with highest closeness centrality (based on “Computing Top-k Closeness Centrality in Fully-dynamic Graphs”, P. Bisenius, E. Bergamini, E. Angriman and H. Meyerhenke. To appear at ALENEX 2018).

Minor:

Dynamic algorithm for updating the weakly connected components of a directed graph after edge additions or removals.

Official support for Windows 10. Take a look at the Get Started guide for further instructions.

Support for SCons3. There are no more dependencies on Python 2 if you decide to use SCons3 with Python 3.

Improved include of external libraries. These can now simply be specified in the build.conf file. See Pull Request #58 for further details.

Today we announce the next version of NetworKit, the open-source toolkit for large-scale network analysis. NetworKit is a Python package, with performance-critical algorithms implemented in C++/OpenMP.

**Release notes**

Major:

Weakly connected components (components.WeaklyConnectedComponents)

Dynamic algorithm for updating connected components in undirected graphs (components.DynConnectedComponents)

Algorithm for computing the weakly connected components in directed graphs (components.WeaklyConnectedComponents)

Enumeration of all simple paths between two nodes, up to a user-specified threshold (distance.AllSimplePaths)

Minor:

Improved documentation

Marked SSSP::getStack() as deprecated and replaced with SSSP::getNodesSortedByDistance()

Several fixes in the LFR generator

Added a wrapper class for the BTER implementation FEASTPACK

Expose restoreNode method to Python

Added shared library option to SCons

The first NetworKit Day will be held on September 12, 2017 at the Karlsruhe Institute of Technology, Karlsruhe, Germany. For further information, visit the webpage https://networkit.github.io/networkit-day.html

**Release notes**

Major:

New dynamic algorithm for updating exact betweenness centrality after an edge insertion, based on “Faster Betweenness Centrality Updates in Evolving Networks”, Bergamini et al., to appear at SEA 2017 (https://arxiv.org/abs/1704.08592)

New dynamic algorithm for updating APSP after an edge insertion (this is basically the first step of the dynamic betweenness algorithm, with the difference that only distances are updated, and not the number of shortest paths)

New faster algorithm for listing all maximal cliques, based on “Listing All Maximal Cliques in Large Sparse Real-World Graphs”, Eppstein and Strash, SEA 2011 (https://link.springer.com/chapter/10.1007/978-3-642-20662-7_31)

Minor:

New base class DynAlgorithm with a common interface for all dynamic algorithms.

Jupyter Notebook explaining how to use dynamic algorithms in NetworKit.

Renamed ApproxBetweenness2 to EstimateBetweenness.

Moved SSSP, DynSSSP and subclasses to distance module.

Refactored PrioQueue and PrioQueueForInts to have a common interface.

Made deletion of incident edges automatic when deleting a node.

Fixed minor issues and improved documentation of several classes.

Exported Graph::randomEdge(s) to Python.

Marked IndependentSetFinder, FruchtermanReingold, Layouter, MultilevelLayouter, RandomSpanningTree, PseudoRandomSpanningTree and MaxClique as deprecated.

NOTE: The classes marked as deprecated will be permanently deleted with the next release. Please contact us if there are reasons why some of the classes should be kept.

Our paper on computing betweenness centrality in dynamic networks using NetworKit (authors: Bergamini, Meyerhenke, Ortmann, Slobbe) has been accepted for publication at the 16th International Symposium on Experimental Algorithms (SEA17).

The NetworKit team is happy to announce that the NetworKit project has been successfully migrated to GitHub. Please join us on

https://github.com/networkit/networkit

We believe the migration will make it easier for developers to contribute to the project and we hope to bring the advantages of efficient large-scale network analysis to even more people.

**Release notes**

Major:

New graph drawing algorithm for the Maxent-stress model; the algorithm can layout even large graphs quickly. It follows the paper by Gansner et al. with some modifications; the biggest deviation is the use of the LAMG solver for the Laplacian linear systems

Parallel implementation for the approximation of the neighborhood function; class has been refactored from ApproxNeighborhoodFunction to NeighborhoodFunctionApproximation.

New heuristic algorithm for the neighborhood function. It is based on sampling and the breadth-first search and offers more flexibility with regards to the tradeoff between running time and accuracy as the number of samples can be specified by the user. It is also much faster than the approximation algorithm for networks with a high diameter (e.g. road networks).

Minor:

Iterative implementation of components.StronglyConnectedComponents, which is now the new default. For graphs where edges have been deleted, it is recommended to use the recursive implementation, which is still available.

Removed heuristic for vertex diameter estimation from centrality.ApproxBetweenness (now the vertex diameter is estimated as suggested in Riondato, Kornaropoulos: Fast approximation of betweenness centrality through sampling)

Refactoring of the approximation algorithms in the distance group. ApproxNAME -> NAMEApproximation.

Simplified installation procedure: Install required dependencies automatically

Our paper on approximating current-flow closeness centrality using NetworKit (authors: Bergamini, Wegner, Lukarski, Meyerhenke) has been accepted for publication at the 7th SIAM Workshop on Combinatorial Scientific Computing (CSC16).

This is a more of a maintenance release, that fixes the pip package and building with clang is possible again (at least with version 3.8).

Note: You can control which C++ compiler the setup.py of the networkit package is supposed to use with e.g. `CXX=clang++ pip install networkit`

. This may be helpful when the setup fails to detect the compiler.

**Release notes**

Major:

new website

C++ implementation of Lean Algebraic Multigrid (LAMG) by Livne et al. for solving large Laplacian systems serves as backend for various network analysis kernels

centrality module

centrality.TopCloseness: Implementation of a new algorithm for finding the top-k nodes with highest closeness centrality faster than computing it for all nodes (E. Bergamini, M. Borassi, P. Crescenzi, A. Marino, H. Meyerhenke, “Computing Top-k Closeness Centrality Faster in Unweighted Graphs”, ALENEX’16)

generator module:

generator.HyperbolicGenerator: a fast parallel generator for complex networks based on hyperbolic geometry (Looz, Meyerhenke, Prutkin ‘15: Random Hyperbolic Graphs in Subquadratic Time)

Minor:

re-introduced an overview(G)-function that collects and prints some infromation about a graph

updated documentation

some IO bugfixes

graph module:

Subgraph class has been removed, its functionality is now in Graph::subgraphFromNodes(…)

generator module:

Many graph generators now provide fit(G) method that returns an instance of the generator such that generated graphs are similar to the provided one

Improved performance of the BarabasiAlbert generator by implementing Batagelj’s method

distance module:

distance.CommuteTimeDistance: a node distance measure, distance is low when there are many short paths connecting two nodes

Adapted Diameter class to Algorithm convention; diameter algorithm can be chosen via enum in the constructor

Adapted EffectiveDiameter class to Algorithm convention resulting in the classes ApproxEffectiveDiameter, ApproxHopPlot, ApproxNeighborhoodFunction; added exact computation of the Neighborhood Function

centrality module:

centrality.SpanningEdgeCentraliy: edge centrality measure representing the fraction of spanning trees containing the edge

centrality.ApproxCloseness: new algorithm for approximating closeness centrality based on “Computing Classic Closeness Centrality, at Scale”, Cohen et al.

Our paper describing NetworKit as a toolkit for large-scale complex network analysis has been accepted by the Cambridge University Press journal Network Science.

Our paper on sparsification methods for social networks with NetworKit (authors: Linder, Staudt, Hamann, Meyerhenke, Wagner) has been accepted for publication in Social Network Analysis and Mining.

Our paper on approximating betweenness centrality in dynamic networks with NetworKit (authors: Bergamini, Meyerhenke) has been accepted for publication in Internet Mathematics.

Our paper on finding the top-k nodes with highest closeness centrality with NetworKit (authors: Bergamini, Borassi, Crescenzi, Marino, Meyerhenke) has been accepted at the 18th Meeting on Algorithm Engineering and Experiments, ALENEX 2016.

We have just released NetworKit 4.0. Apart from several improvements to algorithms and architecture, the main feature of this release is a new front end for exploratory network analysis.

The new version is now available from the Python Package index. Try upgrading with
`pip3 install —upgrade networkit`

We have released version 3.6 today. Thank you to all contributors. Here are the release notes.

*Release Notes*

Major Updates:

Link Prediction

Link prediction methods try to predict the likelihood of a future or missing connection between two nodes in a given network. The new module networkit.linkprediction contains various methods from the literature.

Edge Sparsification

Sparsification reduces the size of networks while preserving structural and statistical properties of interest. The module networkit.sparsification provides methods for rating edges by importance and then filtering globally by these scores. The methods are described in http://arxiv.org/abs/1505.00564

Further Updates:

Improved support for directed graph in analysis algorithms

Improved support for the Intel compiler

Reader/writer for the GEXF (Gephi) graph file format

EdgeListReader now reads edge list with arbitrary node ids (e.g.strings) when continuous=False; getNodeMap() returns a mapping from file node ids to graph node ids

EdgeListReader/Writer now add weights when reading files/writing graphs to file.

Our paper on the approximation of betweenness centrality in fully-dynamic networks with NetworKit (authors: Bergamini, Meyerhenke) has been accepted at the 23rd European Symposium on Algorithms, ESA 2015.

We have released NetworKit 3.5 a couple days ago. Please upgrade to the latest version to receive a number of improvements. We also appreciate feedback on the new release.

*Release Notes*

This release focused on bugfixes, under-the-hood improvements and refactoring.

Various bugfixes and stability improvements

Abort signal handling: developed mechanism to interrupt long-running algorithms via the ctrl+C command – already supported in community.PLM, centrality.Betweennness, centrality.ApproxBetweenness, centrality.ApproxBetweenness2, centrality.PageRank

Efficient node and edge iteration on the Python layer: G.forEdges, G.forNodes…

Constant-time check if a graph has self-loops: Graph.hasSelfLoops()

networkit.setSeed: set a fixed seed for the random number generator

Refactoring: CoreDecomposition and LocalClusteringCoefficient now in centrality module

Refactoring: introduced Python/Cython base classes: Centrality, CommunityDetector

Removed: CNM community detection algorithm

The GIL (Global Interpreter Lock) is released for many algorithms in order to make it possible to execute multiple computations in parallel in a single Python process.

Improved support for directed graphs in many algorithms

Today we have released version 3.4 of NetworKit, the open-source toolkit for high-performance network analysis. This release brings numerous critical bugfixes as well as useful incremental features and performance optimizations. We are also moving towards consistent interfaces for algorithms. We have also further simplified the installation dependencies.

Thank you to the numerous people who have contributed code to this release.

More information can be found on https://networkit.github.io/. We welcome user feedback and opportunities for collaboration.

Release Notes

Features

- graph
Graph can be copied on Python level

spanning tree/forest (graph.SpanningForest)

algorithms in general * Edmonds-Karp max flow algorithm (flow.EdmondsKarp) * core decomposition works for directed graphs (properties.CoreDecomposition) * algebraic distance, a structural distance measure in graphs (distance.AlgebraicDistance)

- IO
there is no longer a default graph file format

read and write the GML graph file format (graphio.GMLGraphReader/Writer)

conversion of directed to undirected graph (Graph.toUndirected)

reader and writer for the GraphTool binary graph format (graphio.GraphToolBinaryReader)

METIS graph reader supports arbitrary edge weights (graphio.METISGraphReader)

- algebraic
algebraic backend supports rectangular matrices (Matrix.h)

- community detection
turbo mode for PLM community detection algorithm gives a factor 2 speedup at the cost of more memory (community.PLM)

Cut Clustering community detection algorithm (community.CutClustering)

- generators
Erdös-Renyi generator can generate directed graphs (generators.ErdosRenyiGenerator)

configuration model graph generator for generating a random simple graph with exactly the given degree sequence (generators.ConfigurationModelGenerator)

generator for power law degree sequences (generators.PowerlawDegreeSequence)

Bugfixes

GraphMLReader improved (graphio.GraphMLReader)

ConnectedComponents usability improved

KONECT reader (graphio.KONECTGraphReader)

fixed build problem on case-insensitive file systems

closed memory leaks by adding missing destructors on the Cython

improved memory management by adding missing move constructors

DynamicForestFireGenerator fixed

Refactoring

standardization of analysis algorithm interface: parameters given by constructor, computation triggered in run method, results retrieved via getter methods

run methods return self to allow chaining

introducing unit tests on Python layer

Build and Installation

pip installation does no longer require Cython

pip installation does no longer require SCons, minimal build system as fallback if SCons is missing

Our paper on approximating betweenness centrality in dynamic networks with NetworKit (authors: Bergamini, Meyerhenke, Staudt) has been accepted at the 17th Meeting on Algorithm Engineering and Experiments, ALENEX 2015.

In a joint tutorial on Algorithmic methods for network analysis with Dorothea Wagner for the summer school of the DFG priority programme Algorithm Engineering, Henning Meyerhenke introduced NetworKit to the participants. The PhD students from Germany and other European countries successfully solved various network analysis tasks with NetworKit during the tutorial.

Our paper on selective community detection with NetworKit (authors: Staudt, Marrakchi, Meyerhenke) has been accepted at the First International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (in Conjunction with IEEE BigData’14).

NetworKit 3.3 has been released, including the following improvements to our network analysis framework:

renamed package to “networkit” according to Python packaging convention

restructured package to enable “pip install networkit”

improved community detection algorithms

improved diameter algorithms

added support for efficient, arbitrary edge attributes via edge indexing

Eigenvector Centrality & PageRank on basis of scipy

spectral methods for graph partitioning (partitioning.SpectralPartitioner), drawing (viztools.layout.SpectralLayout) and coloring (coloring.SpectralColoring)

new graph generators: stochastic blockmodel (generators.StochasticBlockmodel), Watts-Strogatz model (generators.WattsStrogatzGenerator) and Forest Fire model (generators.DynamicForestFireGenerator)

union find data structure (structures/UnionFind)

simple spanning forest algorithm (graph.SpanningForest)

fast algorithm for partition intersection (community/PartitionIntersection)

hub dominance in communities (community.HubDominance)

reader for Matlab adjacency matrices

support for reading and writing Covers

performance improvements in Gephi streaming interface

NetworKit 3.2 has been released, including major improvements to our network analysis framework:

*Critical Bugfixes*

graph data structure supports directed graphs

optimized connected components algorithm (properties.ParallelConnectedComponents)

faster heuristic algorithm for approximating betweenness centrality (centrality.ApproxBetweenness2)

Gephi support: export of node attributes, Gephi streaming plugin support

graph generators: Dorogovtsev-Mendes model

improved portability (Windows)

overhaul of graph file input

NetworKit, our tool suite for high-performance network analysis, has its own website now!

Christian Staudt gave an introductory talk about the current release of NetworKit. The slides and a video of the talk are available on the Documentation page.

Version 3.1 is an incremental update to our tool suite for high-performance network analysis. Improvements and new features include Eigenvector centrality, PageRank, Betweenness centrality approximation, R-MAT graph generator, BFS/DFS iterators, improved BFS and Dijkstra classes, and improved memory footprint when using large objects on the Python level. More detailed information can be found in the accompanying publication.

NetworKit 3.0 is the next major release of our open-source toolkit for high-performance network analysis. Since the last release in November, NetworKit has received several improvements under the hood as well as an extension of the feature set. What started as a testbed for parallel community detection algorithms has evolved into a diverse set of tools that make it easy to characterize complex networks. This has been successfully scaled to large data sets with up to several billions of edges.

This being an open-source project, we are very interested in incorporating feedback from data analysts and algorithm engineers. Feel free to contact us with any question on how NetworKit could be applied in your field of research.

Second major release of NetworKit. The toolkit has been improved by adding several graph algorithms and an interactive shell based on Python/Cython. We begin a more frequent release cycle.

Initial release of the community detection component. With this release of NetworKit, we would like to encourage reproduction of our results, reuse of code and contributions by the community.