SIMPLE: a simplifying-ensembling framework for parallel community detection from large networks

Wu, Zhang; Gao, Guangliang; Bu, Zhan

doi:10.1007/s10586-015-0504-2

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…Wu et al [29] were also concerned with studying a community detection task, presenting a new framework for detecting parallel communities, called SIMPLifying and Ensembling (SIMPLE). Similarly, Liu et al [30] used the Markov-network for discovering latent links, i.e., links which are not directly observable but rather inferred, among people in social networks.…”

Section: Related Workmentioning

confidence: 99%

Towards business partnership recommendation using user opinion on Facebook

Tsutsumi

Fenerich

Silva

2019

J Internet Serv Appl

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The identification of strategic business partnerships can potentially provide competitive advantages for businesses; however, due to the dynamics and uncertainty present in business environments , this task could be challenging. To help businesses in this task , this study presents a similarity model between businesses that consider the opinions of users on content shared by businesses on social media. Thus, this model captures significant virtual relationships among businesses that are generated by users in the virtual world. Besides, we propose an algorithm for detecting business communities in the considered model. We also propose an algorithm to identify possible business outliers in the detected communities, which could represent an automatic way to identify non-obvious relations that might deserve particular attention of business owners. By exploring approximately 280 million user reactions on Facebook, we show that our results could favor the development of, for example, a new strategic business partnership recommendation service.

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Section: Related Workmentioning

confidence: 99%

Towards business partnership recommendation using user opinion on Facebook

Tsutsumi

Fenerich

Silva

2019

J Internet Serv Appl

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“…Empirical evaluation of Fender's algorithm shows that the algorithm can infer a flat set of clusters from an input graph, while achieving a significant speed‐up at the same time. Wu et al proposed a distributed community detection algorithm for large networks using the Map Reduce parallel programming framework . Wu's algorithm adopts a three‐stage approach in which the network is sparsified by randomly sampling interconnections among the network nodes.…”

Section: Related Workmentioning

confidence: 99%

“…The sets of clusters generated by different parallel processes are then reconciled through k‐means to output the final set of clusters. This process of reconciliation is referred to as consensus clustering . As this reconciliation is analogous to the bagging technique, it is called as the ensemble clustering .…”

Section: Related Workmentioning

confidence: 99%

“…Wu et al proposed a distributed community detection algorithm for large networks using the Map Reduce parallel programming framework. 12 Wu's algorithm adopts a three-stage approach in which the network is sparsified by randomly sampling interconnections among the network nodes. The sparsified network is then clustered by multiple parallel processes each running on the same sparsified network.…”

Section: Related Workmentioning

confidence: 99%

“…This process of reconciliation is referred to as consensus clustering. 12 As this reconciliation is analogous to the bagging technique, it is called as the ensemble clustering. Leng et al proposed a technique based on multi-level modularity optimization to cluster data in the Resource Description Framework (RDF) format, so that the clusters represent a balanced partitioning.…”

Section: Related Workmentioning

confidence: 99%

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Summary We propose a distributed parallel algorithm for inferring the hierarchical groups present in a large‐scale text corpus. The algorithm is designed to deal with corpuses that typically do not fit into the main memory of a workstation computer. The key contribution of this paper lies in its proposal and verification of a parallel distributed algorithm that exploits the advantages of two complementary techniques based on (i) localized modularity optimization and (ii) spectral clustering. Based on our experimental observations, these are complementary in the sense that the former excels at finding coarse groups in a large‐scale network, while the latter demands a heavy memory footprint but is effective in inferring tightly knit fine‐grained groups. Empirical evaluation of the distributed implementation scheme shows that the algorithm exhibits a significant speed‐up when compared to existing algorithms like Louvain and, at the same time, produces better quality clusters than either Louvain or spectral clustering algorithms in terms of the F‐score and Rand index.

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DFuzzy: a deep learning-based fuzzy clustering model for large graphs

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SIMPLE: a simplifying-ensembling framework for parallel community detection from large networks

Cited by 9 publications

References 30 publications

Towards business partnership recommendation using user opinion on Facebook

Towards business partnership recommendation using user opinion on Facebook

A distributed parallel algorithm for inferring hierarchical groups from large‐scale text corpuses

DFuzzy: a deep learning-based fuzzy clustering model for large graphs

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