Balanced Team Formation Using Hybrid Graph Convolution Networks and MILP
Source abstract
Abstract
In this paper, we propose a novel model that is based on a hybrid paradigm composed of a graph convolution network and an Integer Programming solver. The model utilizes the potential of graph neural networks, which have the ability to capture complex relationships and preferences among nodes. While the graph neural network forms node embeddings that are fed as input into the next layer of the model, the introduced MILP solver works to solve the team formation problem. Finally, our experimental work shows that the outcome of the model is balanced teams.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Team Dynamics and Performance · Insect and Arachnid Ecology and Behavior · Complex Network Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering and Networks, College of Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
Funding
Fast-Track Research Funding Program
References (31)
Show source-provided references
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