Fast parallel algorithm for unfolding of communities in large graphs

Wickramaarachchi, Charith; Frîncu, Marc; Small, Patrick; Prasanna, Viktor K.

doi:10.1109/hpec.2014.7040973

Cited by 42 publications

(22 citation statements)

References 21 publications

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“…Fig. 9 shows the modularity difference between the Louvain algorithm and the Louvain Prune algorithm by calculating the Error percentage [13]. Error E is defined as…”

Section: Experimental Results -Accuracymentioning

confidence: 99%

A Simple Acceleration Method for the Louvain Algorithm

Ozaki¹,

Tezuka²,

Inaba³

2016

IJCEE

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Abstract:The Louvain algorithm is well known for its high speed for detecting community structure in networks. In this paper, first, we analyze the Louvain algorithm as the preliminary experiment to uncover the processes that cause wasted computational time and their characteristics. Then based on this, we propose the Louvain Prune algorithm. The experiments show that the Louvain Prune algorithm significantly reduces computational time by up to 90%, and retains almost the same quality as the original Louvain algorithm.

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“…Fig. 9 shows the modularity difference between the Louvain algorithm and the Louvain Prune algorithm by calculating the Error percentage [13]. Error E is defined as…”

Section: Experimental Results -Accuracymentioning

confidence: 99%

A Simple Acceleration Method for the Louvain Algorithm

Ozaki¹,

Tezuka²,

Inaba³

2016

IJCEE

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“…The work by Wickramaarachchi et al [25] targets distributed memory parallelism, with the primary approach being to use a graph partitioner to partition the input graph a priori and subsequently run the sequential algorithm on each part separately (ignoring the contribution from cross-partition edges) before merging the results through an aggregation process at a master processor. In another parallel effort, Staudt and Meyerhenke [26] present an alternative approach called PLM that uses label propagation to parallelize the Louvain method.…”

Section: Related Workmentioning

confidence: 99%

Parallel heuristics for scalable community detection

2015

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a b s t r a c tCommunity detection has become a fundamental operation in numerous graph-theoretic applications. It is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is a multi-phase, iterative heuristic for modularity optimization. Originally developed by Blondel et al. (2008), the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memoryefficient manner. However, the method is also inherently sequential, thereby limiting its scalability. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose heuristics that are designed to break the sequential barrier. For evaluation purposes, we implemented our heuristics using OpenMP multithreading, and tested them over real world graphs derived from multiple application domains (e.g., internet, citation, biological). Compared to the serial Louvain implementation, our parallel implementation is able to produce community outputs with a higher modularity for most of the inputs tested, in comparable number or fewer iterations, while providing absolute speedups of up to 16Â using 32 threads.

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“…A default strategy is to traverse nodes in a random order. In [27] the authors evaluate several other vertex ordering strategies and suggest that sort nodes based on descending order of edge degree can bring marginal improvement on computation time than the default strategy. Results in [28] also show that partitions generated by Louvain algorithm following this degree-descending order can have low variance in modularity value (the number in most tested networks is at the level of 10 −5 ).…”

Section: Generating Options From Communitiesmentioning

confidence: 99%

Constructing Temporally Extended Actions through Incremental Community Detection

Mei

2018

Computational Intelligence and Neuroscience

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Hierarchical reinforcement learning works on temporally extended actions or skills to facilitate learning. How to automatically form such abstraction is challenging, and many efforts tackle this issue in the options framework. While various approaches exist to construct options from different perspectives, few of them concentrate on options' adaptability during learning. This paper presents an algorithm to create options and enhance their quality online. Both aspects operate on detected communities of the learning environment's state transition graph. We first construct options from initial samples as the basis of online learning. Then a rule-based community revision algorithm is proposed to update graph partitions, based on which existing options can be continuously tuned. Experimental results in two problems indicate that options from initial samples may perform poorly in more complex environments, and our presented strategy can effectively improve options and get better results compared with flat reinforcement learning.

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Fast parallel algorithm for unfolding of communities in large graphs

Cited by 42 publications

References 21 publications

A Simple Acceleration Method for the Louvain Algorithm

A Simple Acceleration Method for the Louvain Algorithm

Parallel heuristics for scalable community detection

Constructing Temporally Extended Actions through Incremental Community Detection

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