IEEE

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...
TMCnet

MicroCloud Hologram Inc. Releases Learnable Quantum Spectral Filter Technology for Hybrid Graph Neural Networks

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...
IEEE

Ensuring Fairness in Spectral Clustering via Disparate Impact-Based Graph Construction

Abstract: Spectral clustering algorithms rely on graphs where edges are defined based on the similarity between the vertices (data points). The effectiveness and fairness of spectral clustering depend ...
Frontiers

Density peak clustering algorithm based on weighted mutual K-nearest neighbors

1 School of Computer Science and Technology, Yibin University, Yibin, China 2 School of Computer and Software, Southwest Petroleum University, Chengdu, China Ever since Density Peak Clustering (DPC) ...
Time

How the Secret Algorithms Behind Social Media Actually Work

Ever wondered how social media platforms decide how to fill our feeds? They use algorithms, of course, but how do these algorithms work? A series of corporate leaks over the past few years provides a ...
GitHub

graph-clustering-algorithm

This is a project on segmenting hindi and other Indian languages for easier translation and convenience in other nlp tasks.
marktechpost

KGGen: Advancing Knowledge Graph Extraction with Language Models and Clustering Techniques

Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack ...