NetworKit

Large-Scale Network Analysis

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.