Gradient descent clustering with regularization to recover communities in transformed attributed networks

Social Network Analysis and Mining. 2025. Vol. 15212. P. 137–148.

Shalileh S.

Community detection in attributed networks aims to recover clusters in which the within-community nodes are as interconnected and as homogeneous as possible, while the between-communities nodes are as disconnected and as heterogeneous as possible. The current research proposes a straightforward data-driven model with an integrated regularization term to recover communities. For further improvement of the quality of detected communities we also propose a softmax-scaled-dot-product to transform the data spaces into more cluster-friendly data spaces. We adopt the gradient descent optimization strategy to optimize our proposed clustering objective function. We compare the performance of the proposed method using both real-world and synthetic data sets with three state-of-art algorithms. Our results showed that the proposed method obtains promising result

Keywords: clustering, community detection, gradient descent, attributed network, Feature-Rich Network

Publication based on the results of: A multi-model computational approach to studying human brain function: visual perception and other functions (2024)