NetworKit: A tool suite for large-scale complex network analysis

Staudt, Christian; Sazonovs, Aleksejs; Meyerhenke, Henning

doi:10.1017/nws.2016.20

Cited by 208 publications

(159 citation statements)

References 52 publications

(70 reference statements)

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“…In order to prevent trivial minimum cuts, we use a power law exponent of 5. We use the generator of von Looz et al [39], which is a part of NetworKit [36], to generate unweighted random hyperbolic graphs with 2 20 to 2…”

Section: Discussionmentioning

confidence: 99%

Practical Minimum Cut Algorithms

Henzinger¹,

Noe²,

Schulz³

et al. 2018

2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)

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The minimum cut problem for an undirected edgeweighted graph asks us to divide its set of nodes into two blocks while minimizing the weight sum of the cut edges. Here, we introduce a linear-time algorithm to compute near-minimum cuts. Our algorithm is based on cluster contraction using label propagation and Padberg and Rinaldi's contraction heuristics [SIAM Review, 1991]. We give both sequential and shared-memory parallel implementations of our algorithm. Extensive experiments on both real-world and generated instances show that our algorithm finds the optimal cut on nearly all instances significantly faster than other state-of-theart exact algorithms, and our error rate is lower than that of other heuristic algorithms. In addition, our parallel algorithm shows good scalability.

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Section: Discussionmentioning

confidence: 99%

Practical Minimum Cut Algorithms

Henzinger¹,

Noe²,

Schulz³

et al. 2018

2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)

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“…For all of these local community detection algorithms, we provide novel or improved implementations in C++ as part of the open source network analysis tool suite NetworKit [13]. The exact implementations used and the scripts used to generate graphs, evaluate the detected communities and generate the plots are available online [14].…”

Section: Methodsmentioning

confidence: 99%

“…Further we also perform experiments on real-world social networks. We implemented all examined algorithms in the network analysis toolkit NetworKit [13] and make them publicly available [14], we aim to make them part of the official NetworKit code base. We show that starting from a clique dramatically improves the accuracy of the detected communities both on synthetic and on real-world networks.…”

Section: Introductionmentioning

confidence: 99%

Local Community Detection Based on Small Cliques

2017

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Community detection aims to find dense subgraphs in a network. We consider the problem of finding a community locally around a seed node both in unweighted and weighted networks. This is a faster alternative to algorithms that detect communities that cover the whole network when actually only a single community is required. Further, many overlapping community detection algorithms use local community detection algorithms as basic building block. We provide a broad comparison of different existing strategies of expanding a seed node greedily into a community. For this, we conduct an extensive experimental evaluation both on synthetic benchmark graphs as well as real world networks. We show that results both on synthetic as well as real-world networks can be significantly improved by starting from the largest clique in the neighborhood of the seed node. Further, our experiments indicate that algorithms using scores based on triangles outperform other algorithms in most cases. We provide theoretical descriptions as well as open source implementations of all algorithms used.

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“…In order to show experimentally that we achieved this goal, we generated graphs with identical parameters using the original LFR implementation and EM-LFR. For disjoint clusters we also compare it with the implementation that is part of NetworKit [35]. Using NetworKit, we evaluate the results of Infomap [32], Louvain [7] and OSLOM [23], three stateof-the-art clustering algorithms [8,11,14], and compare them using the adjusted rand measure [19] and NMI [12].…”

Section: Qualitative Comparison Of Em-lfrmentioning

confidence: 99%

I/O-efficient Generation of Massive Graphs Following the LFR Benchmark

Hamann

Meyer

Penschuck

et al. 2017

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

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LFR is a popular benchmark graph generator used to evaluate community detection algorithms. We present EM-LFR, the first external memory algorithm able to generate massive complex networks following the LFR benchmark. Its most expensive component is the generation of random graphs with prescribed degree sequences which can be divided into two steps: the graphs are first materialized deterministically using the Havel-Hakimi algorithm, and then randomized. Our main contributions are EM-HH and EM-ES, two I/O-efficient external memory algorithms for these two steps. In an experimental evaluation we demonstrate their performance: our implementation is able to handle graphs with more than 37 billion edges on a single machine, is competitive with a massive parallel distributed algorithm, and is faster than a state-of-the-art internal memory implementation even on instances fitting in main memory. EM-LFR's implementation is capable of generating large graph instances orders of magnitude faster than the original implementation. We give evidence that both implementations yield graphs with matching properties by applying clustering algorithms to generated instances.

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NetworKit: A tool suite for large-scale complex network analysis

Cited by 208 publications

References 52 publications

Practical Minimum Cut Algorithms

Practical Minimum Cut Algorithms

Local Community Detection Based on Small Cliques

I/O-efficient Generation of Massive Graphs Following the LFR Benchmark

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