Approximation Algorithms for Minimum K -Cut

Guttmann‐Beck, Nili; Hassin, Refael

doi:10.1007/s004530010013

Cited by 25 publications

(24 citation statements)

References 4 publications

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“…Hence the overall asymptotic complexity of Min-Star-Partition is O(n x+1 ) time, which is polynomial when x is a constant. Please refer [17] for further details.…”

Section: Algorithm 3 Algorithm Min-star-partitionmentioning

confidence: 99%

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Task Assignment Optimization in Collaborative Crowdsourcing

Rahman¹,

Roy²,

Thirumuruganathan³

et al. 2015

2015 IEEE International Conference on Data Mining

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A number of emerging applications, such as, collaborative document editing, sentence translation, and citizen journalism require workers with complementary skills and expertise to form groups and collaborate on complex tasks. While existing research has investigated task assignment for knowledge intensive crowdsourcing, they often ignore the aspect of collaboration among workers, that is central to the success of such tasks. Research in behavioral psychology has indicated that large groups hinder successful collaboration. Taking that into consideration, our work is one of the first to investigate and formalize the notion of collaboration among workers and present theoretical analyses to understand the hardness of optimizing task assignment. We propose efficient approximation algorithms with provable theoretical guarantees and demonstrate the superiority of our algorithms through a comprehensive set of experiments using real-world and synthetic datasets. Finally, we conduct a real world collaborative sentence translation application using Amazon Mechanical Turk that we hope provides a template for evaluating collaborative crowdsourcing tasks in micro-task based crowdsourcing platforms.

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“…Hence the overall asymptotic complexity of Min-Star-Partition is O(n x+1 ) time, which is polynomial when x is a constant. Please refer [17] for further details.…”

Section: Algorithm 3 Algorithm Min-star-partitionmentioning

confidence: 99%

“…The proof uses an easy reduction from [17]. We start by showing how the solution to SpltBOpt problem could be bounded by the solution of a slightly different problem variant, known as Min-Star problem [17].…”

Section: Algorithm 3 Algorithm Min-star-partitionmentioning

confidence: 99%

Task Assignment Optimization in Collaborative Crowdsourcing

Rahman¹,

Roy²,

Thirumuruganathan³

et al. 2015

2015 IEEE International Conference on Data Mining

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“…By the analysis of T. Tokuyama [13], the min-star problem can be solved in O(n). According to [14], the following result shows the connection between the min-star problem and Problem 5. Lemma 4 implies that suppose one of the terminal sets of the optimal partition is known to us, we have already had an efficient approximation algorithm for the equal-sized community detection problem.…”

Section: Problem 4 Equal-sized Community Detection Problemmentioning

confidence: 99%

Terminal-set-enhanced community detection in social networks

Tong

Cui

et al. 2016

IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications

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“…Even though the number of subgroups (aka partitions) is n K with K workers in all but last subgroup, finding an optimal assignment of the n workers across those subgroups that minimizes the objective function is NP-hard. The proof uses an easy reduction from [20]. We start by showing how the solution to SpltBOpt problem could be bounded by the solution of a slightly different problem variant, known as Min-Star problem [20].…”

Section: α# ####################α# A# C# D# E# ####################αmentioning

confidence: 99%

Optimized group formation for solving collaborative tasks

et al. 2018

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Many popular applications, such as collaborative document editing, sentence translation, or citizen science resort to collaborative crowdsourcing, a special form of human-based computing, where, crowd workers with appropriate skills and expertise are required to form groups to solve complex tasks. While there has been extensive research on workers' task assignment for traditional microtask based crowdsourcing, they often ignore the critical aspect of collaboration. Central to any collaborative crowdsourcing process is the aspect of solving collaborative tasks that requires successful collaboration among the workers. Our formalism considers two main collaboration-related factors-affinity and upper critical mass-appropriately adapted from organizational science and social theories. Our contributions are three fold. First, we formalize the notion of collaboration among crowd workers and propose a comprehensive optimization model for task assignment in a collaborative crowdsourcing environment. Next, we study the hardness of the task assignment optimization problem and propose a series of efficient exact and ap

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Approximation Algorithms for Minimum K -Cut

Cited by 25 publications

References 4 publications

Task Assignment Optimization in Collaborative Crowdsourcing

Task Assignment Optimization in Collaborative Crowdsourcing

Terminal-set-enhanced community detection in social networks

Optimized group formation for solving collaborative tasks

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