# Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation

arXiv: https://arxiv.org/abs/1409.5512 · HTML: https://mlsanity.com/paper/1409.5512/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2014.

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network](https://mlsanity.com/paper/2211.04100/) (2022)

## Abstract (arXiv)

In this paper, we study the problem of Team Member Replacement: given a team of people embedded in a social network working on the same task, find a good candidate who can fit in the team after one team member becomes unavailable. We conjecture that a good team member replacement should have good skill matching as well as good structure matching. We formulate this problem using the concept of graph kernel. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall; and (b) scale sub-linearly.