# Boosting Team Modeling through Tempo-Relational Representation Learning

arXiv: https://arxiv.org/abs/2507.13305 · HTML: https://mlsanity.com/paper/2507.13305/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 163 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer](https://mlsanity.com/paper/2108.12630/) (2021)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [Learning Efficient Multi-Agent Cooperative Visual Exploration](https://mlsanity.com/paper/2110.05734/) (2021)
- [Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach](https://mlsanity.com/paper/2208.11660/) (2022)
- [Language and Sketching: An LLM-driven Interactive Multimodal Multitask Robot Navigation Framework](https://mlsanity.com/paper/2311.08244/) (2023)
- [Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs](https://mlsanity.com/paper/2406.15156/) (2024)
- [First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis](https://mlsanity.com/paper/1804.08046/) (2018)
- [SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization](https://mlsanity.com/paper/2410.21411/) (2024)
- [Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare](https://mlsanity.com/paper/2102.08507/) (2021)
- [A Survey of Multi-Agent Human-Robot Interaction Systems](https://mlsanity.com/paper/2212.05286/) (2022)
- [Automated Task-Time Interventions to Improve Teamwork using Imitation Learning](https://mlsanity.com/paper/2303.00413/) (2023)
- [Backchannel Detection and Agreement Estimation from Video with Transformer Networks](https://mlsanity.com/paper/2306.01656/) (2023)
- [Analyzing Verbal and Nonverbal Features for Predicting Group Performance](https://mlsanity.com/paper/1907.01369/) (2019)
- [MultiMediate'24: Multi-Domain Engagement Estimation](https://mlsanity.com/paper/2408.16625/) (2024)
- [A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction](https://mlsanity.com/paper/1901.09002/) (2019)

## Built on by (most cited first, 3 of 3)

- [Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs](https://mlsanity.com/paper/2605.04169/) (2026)
- [From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams](https://mlsanity.com/paper/2608.23254/) (2026)
- [Towards Actionable Surgical Team Dynamics: from Teamwork to Counterfactual Annotations](https://mlsanity.com/paper/2608.23344/) (2026)

## Abstract (arXiv)

Team modeling remains a fundamental challenge at the intersection of Artificial Intelligence and Social Sciences. Although a variety of computational models have been proposed in the last two decades, most fail to integrate Social Sciences insights, such as the critical role of temporal interactions in shaping team dynamics, and do not meet key practical requirements for real-world applications, including the ability to provide real-time, actionable recommendations to enhance team performance. To address these limitations, in this paper, we propose a novel tempo-relational neural architecture that jointly models interactions between team members and the evolution of team dynamics through temporal graphs. We additionally propose a multi-task extension of the architecture that learns shared social embeddings for team members enabling the simultaneous prediction of multiple team constructs (e.g., Emergent Leadership, Leadership Style, and Teamwork components). Experiments on two state-of-the-art team datasets show that our tempo-relational architecture out performs temporal-only and relational-only approaches for team performance prediction, and that its multi-task extension substantially reduces training and inference time without loss of predictive performance. Finally, the integration of explainability techniques within the proposed architectures provides interpretable insights and actionable recommendations to support team improvement. These strengths make our approach particularly well-suited for human-centered artificial intelligence applications, such as intelligent decision-support systems in high-stakes collaborative environments.