# Explaining Expert Search and Team Formation Systems with ExES

arXiv: https://arxiv.org/abs/2405.12881 · HTML: https://mlsanity.com/paper/2405.12881/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 65 references that are in the map.
- First posted on arXiv 21 May 2024.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks](https://mlsanity.com/paper/2102.03322/) (2021)
- [Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking](https://mlsanity.com/paper/2306.10453/) (2023)
- [EXS: Explainable Search Using Local Model Agnostic Interpretability](https://mlsanity.com/paper/1809.03857/) (2018)
- [Unsupervised, Efficient and Semantic Expertise Retrieval](https://mlsanity.com/paper/1608.06651/) (2016)
- [Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank](https://mlsanity.com/paper/2212.12722/) (2022)
- [Deep Neural Networks for Optimal Team Composition](https://mlsanity.com/paper/1805.03285/) (2018)
- [WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking](https://mlsanity.com/paper/1805.03947/) (2018)
- [BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer](https://mlsanity.com/paper/2007.07229/) (2020)
- [Expert Finding in Legal Community Question Answering](https://mlsanity.com/paper/2201.07667/) (2022)
- [CREDENCE: Counterfactual Explanations for Document Ranking](https://mlsanity.com/paper/2302.04983/) (2023)
- [Authority-based Team Discovery in Social Networks](https://mlsanity.com/paper/1611.02992/) (2016)
- [ExEm: Expert Embedding using dominating set theory with deep learning approaches](https://mlsanity.com/paper/2001.08503/) (2020)
- [New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding](https://mlsanity.com/paper/2004.03621/) (2020)
- [Quality-aware skill translation models for expert finding on StackOverflow](https://mlsanity.com/paper/1907.06836/) (2019)
- [ExpFinder: An Ensemble Expert Finding Model Integrating N-gram Vector Space Model and μCO-HITS](https://mlsanity.com/paper/2101.06821/) (2021)

## Abstract (arXiv)

Expert search and team formation systems operate on collaboration networks, with nodes representing individuals, labeled with their skills, and edges denoting collaboration relationships. Given a keyword query corresponding to the desired skills, these systems identify experts that best match the query. However, state-of-the-art solutions to this problem lack transparency. To address this issue, we propose ExES, a tool designed to explain expert search and team formation systems using factual and counterfactual methods from the field of explainable artificial intelligence (XAI). ExES uses factual explanations to highlight important skills and collaborations, and counterfactual explanations to suggest new skills and collaborations to increase the likelihood of being identified as an expert. Towards a practical deployment as an interactive explanation tool, we present and experimentally evaluate a suite of pruning strategies to speed up the explanation search. In many cases, our pruning strategies make ExES an order of magnitude faster than exhaustive search, while still producing concise and actionable explanations.