Kemeny Constant-Based Optimization of Network Clustering Using Graph Neural Networks.

ABSTRACT: The recent trend in using network and graph structures to represent a variety of different data types has renewed interest in the graph partitioning (GP) problem. This interest stems from the need for general methods that can both efficiently identify network communities and reduce the dimensionality of large graphs while satisfying various application-specific criteria. Traditional clustering algorithms often struggle to capture the complex relationships within graphs and generalize to arbitrary clustering criteria. The emergence of graph neural networks (GNNs) as a powerful framework for learning representations of graph data provides new approaches to solving the problem. Previous work has shown GNNs to be capable of proposing partitionings using a variety of criteria. However, these ap

SUBMITTER: Martino SA 

PROVIDER: S-EPMC11367579 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature