EXTRA: Explaining Team Recommendation in Networks

TL;DR

This paper introduces EXTRA, an interactive system that explains team recommendations in networks by providing visual insights and a fast algorithm for understanding the underlying graph kernel, enhancing interpretability.

Contribution

The paper presents the first interactive system for explaining team recommendation algorithms in networks, including a fast explanation algorithm and intuitive visual analysis tools.

Findings

Effective and fast explanation algorithm for random walk graph kernel

Enhanced interpretability of team recommendation results

User-friendly visual explanations for network-based recommendations

Abstract

State-of-the-art in network science of teams offers effective recommendation methods to answer questions like who is the best replacement, what is the best team expansion strategy, but lacks intuitive ways to explain why the optimization algorithm gives the specific recommendation for a given team optimization scenario. To tackle this problem, we develop an interactive prototype system, EXTRA, as the first step towards addressing such a sense-making challenge, through the lens of the underlying network where teams embed, to explain the team recommendation results. The main advantages are (1) Algorithm efficacy: we propose an effective and fast algorithm to explain random walk graph kernel, the central technique for networked team recommendation; (2) Intuitive visual explanation: we present intuitive visual analysis of the recommendation results, which can help users better understand the rationality of the underlying team recommendation algorithm.