# GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network

arXiv: https://arxiv.org/abs/2211.04100 · HTML: https://mlsanity.com/paper/2211.04100/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 22 of 22)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Adaptive Universal Generalized PageRank Graph Neural Network](https://mlsanity.com/paper/2006.07988/) (2020)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Few-shot Network Anomaly Detection via Cross-network Meta-learning](https://mlsanity.com/paper/2102.11165/) (2021)
- [GraKeL: A Graph Kernel Library in Python](https://mlsanity.com/paper/1806.02193/) (2018)
- [Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation](https://mlsanity.com/paper/1409.5512/) (2014)
- [REFORM: Fast and Adaptive Solution for Subteam Replacement](https://mlsanity.com/paper/2101.11070/) (2021)
- [EXTRA: Explaining Team Recommendation in Networks](https://mlsanity.com/paper/1809.08511/) (2018)

## 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)
- [Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails](https://mlsanity.com/paper/2508.20328/) (2025)

## Abstract (arXiv)

Subteam replacement is defined as finding the optimal candidate set of people who can best function as an unavailable subset of members (i.e., subteam) for certain reasons (e.g., conflicts of interests, employee churn), given a team of people embedded in a social network working on the same task. Prior investigations on this problem incorporate graph kernel as the optimal criteria for measuring the similarity between the new optimized team and the original team. However, the increasingly abundant social networks reveal fundamental limitations of existing methods, including (1) the graph kernel-based approaches are powerless to capture the key intrinsic correlations among node features, (2) they generally search over the entire network for every member to be replaced, making it extremely inefficient as the network grows, and (3) the requirement of equal-sized replacement for the unavailable subteam can be inapplicable due to limited hiring budget. In this work, we address the limitations in the state-of-the-art for subteam replacement by (1) proposing GENIUS, a novel clustering-based graph neural network (GNN) framework that can capture team network knowledge for flexible subteam replacement, and (2) equipping the proposed GENIUS with self-supervised positive team contrasting training scheme to improve the team-level representation learning and unsupervised node clusters to prune candidates for fast computation. Through extensive empirical evaluations, we demonstrate the efficacy of the proposed method (1) effectiveness: being able to select better candidate members that significantly increase the similarity between the optimized and original teams, and (2) efficiency: achieving more than 600 times speed-up in average running time.