Multi-Skill Collaborative Teams based on Densest Subgraphs

SDM 2012
Multi-Skill Collaborative Teams based on Densest Subgraphs
Atish Das Sarma, Amita Gajewar, Atish Das Sarma, Amita Gajewar
Abstract

We consider the problem of identifying a team of skilled individuals for collaboration, in the presence of a social network, with the goal to maximize the collaborative compatibility of the team. Each node in the social network is associated with skills, and edge-weights specify affinity between respective nodes. We measure collaborative compatibility objective as the density of the induced subgraph on selected nodes.

This problem is NP-hard even when the team requires individuals of only one skill. We present a 3-approximation algorithm for the single-skill team formulation problem. We show the same approximation can be extended to a special case of multiple skills.

Our problem generalizes the formulation studied by Lappas et al. [KDD ’09] who measure team compatibility in terms of diameter or spanning tree. The experimental results show that the density-based algorithms outperform the diameter-based objective on several metrics.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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