This repository contains the code for the following paper,
GENIUS: Subteam Replacement with Clustering-based Graph Neural Networks (SDM'24)
Chuxuan Hu, Qinghai Zhou, and Hanghang Tong
In this paper, we introduce GENIUS, a clustering-based graph neural network (GNN) framework that (1) captures team social network knowledge for subteam replacement by deploying team-level attention GNNs (TAGs) and self-supervised positive team contrasting training scheme, (2) generates unsupervised team social network member clusters to prune candidates for fast computation, and (3) incorporates a subteam recommender that selects new subteams of flexible sizes. We demonstrate the efficacy of the proposed method in terms of (1) effectiveness: being able to select better subteam members that significantly increase the similarity between the new and original teams, and (2) efficiency: achieving more than 600 times speed-up in average running time. Please refer to our paper for more details.
genius.py : Implementation for the entire GENIUS framework, including the team encoder, subteam recommender, and the training schema.
tag.py : Implementation for the team encoder with the Team-level Attention GNNs (TAGs)
evaluation.py : The evaluation code using 3 different graph similarity metrics as mentioned in Section 4.1.
dblp.py: Code for loading data using the DBLP dataset as an example.