Skip to content
Log in
#graph neural networks Open access

Topology-Based Generative Algorithms – Self-Organizing Neural Networks

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Applications

Abstract

This paper explores the application of topology-based generative algorithms to neural networks, aiming to design novel and complex architectures through a self-organizing learning paradigm. We propose a graph-based approach where the network's structure is explicitly defined by the topology of the input data, offering a shift away from purely random networks. The core mechanism involves learning the network's topology and using this topology to guide the learning process, leading to architectures with enhanced predictability and the potential for generating complex patterns. We present a preliminary design and analysis demonstrating the effectiveness of this approach in generating novel neural network topologies.

DOI
View source

Similar papers

#graph neural networks Oct 2026

Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This article presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPPs). Unlike conventional machine learning–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data’s intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexity O(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom MPC. Compared to existing model-free MPC and deep learning neural network, the LPP-MPC has the lowest THD and root mean square error with the least computational time owing to its efficient linear structure and strong generalization capability.

Jianwu Zeng, Lizheng Cheng, V. Winstead et al. · 1 citation

Related blog posts