Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

Regunta, Sai Charan; Tondomker, Sai Harsh; Shukla, Kshitij; Kothapalli, Kishore

doi:10.1002/cpe.6650

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…Therefore, parallel algorithms for updating graph analytics over dynamic graphs are gaining significant research attention in recent years. Examples include the dynamic computation of centrality scores [3,4,5,6,7], dynamic maintenance of biconnected components [8,9,10], dynamic computation of shortest paths [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection

Sahu¹

2023

Preprint

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Community detection is an essential task in network analysis as it helps identify groups and patterns within a network. High-speed community detection algorithms are necessary to analyze large-scale networks in a reasonable amount of time. Researchers have made significant contributions in the development of high-speed community detection algorithms, particularly in the area of label-propagation based disjoint community detection. These algorithms have been proven to be highly effective in analyzing large-scale networks in a reasonable amount of time. However, it is important to evaluate the performance and accuracy of these existing methods to determine which algorithm is best suited for a particular type of network and specific research problem. In this report, we investigate the RAK, COPRA, and SLPA, three label-propagation-based static community discovery techniques. We pay close attention to each algorithm's minute details as we implement both its single-threaded and multi-threaded OpenMP-based variants, making any necessary adjustments or optimizations and obtaining the right parameter values. The RAK algorithm is found to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with 12 threads was 6.75x faster than the sequential non-strict RAK. The COPRA algorithm works well with a single label for road networks and max labels of 4-16 for other classes of graphs. The SLPA algorithm performs well with increasing memory size, but overall doesn't offer a favourable return on investment. The RAK algorithm is recommended for label-propagation based disjoint community detection.

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Section: Introductionmentioning

confidence: 99%

Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection

Sahu¹

2023

Preprint

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“…As a result, research into parallel algorithms for analyzing and updating graph analytics on dynamic graphs has gained significant attention in recent years. Examples of this research include the dynamic calculation of centrality scores [3,4,5,6,7], maintenance of biconnected components [8,9,10], and computation of shortest paths [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Efficiency in Parallel Louvain Algorithm for Community Detection

Sahu¹

2023

Preprint

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Community detection is a key aspect of network analysis, as it allows for the identification of groups and patterns within a network. With the ever-increasing size of networks, it is crucial to have fast algorithms to analyze them efficiently. It is a modularity-based greedy algorithm that divides a network into disconnected communities better over several iterations. Even in big, dense networks, it is renowned for establishing high-quality communities. However it can be at least a factor of ten slower than community discovery techniques that rely on label-propagation, which are generally extremely fast but obtain communities of lower quality. The researchers have suggested a number of methods for parallelizing and improving the Louvain algorithm. To decide which strategy is generally the best fit and which parameter values produce the highest performance without compromising community quality, it is critical to assess the performance and accuracy of these existing approaches. As we implement the single-threaded and multi-threaded versions of the static Louvain algorithm in this report, we carefully examine the method's specifics, make the required tweaks and optimizations, and determine the right parameter values. The tolerance between each pass can be changed to adjust the method's performance. With an initial tolerance of 0.01 and a tolerance decline factor of 10, an asynchronous version of the algorithm produced the best results. Generally speaking, according to our findings, the approach is not well suited for shared-memory parallelism; however, one potential workaround is to break the graph into manageable chunks that can be independently executed and then merged back together.

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