Jan 17, 2022 · In this paper, we propose a more practical GSL paradigm, unsupervised graph structure learning, where the learned graph topology is optimized by data itself without any external guidance (i.e., labels).

Jan 1, 2020 · This paper presents an approach based on unsupervised learning techniques for the grouping of traces to generate simpler and more understandable models. The algorithms implemented for clustering are K-means, hierarchical agglomerative and density-based spatial clustering of applications with noise (DBSCAN).

Jan 1, 2023 · To address the two issues, this paper develops an unsupervised graph-level representation learning framework named H ierarchical G raph C ontrastive L earning (HGCL), which investigates the hierarchical structural semantics of a graph at both node and graph levels.

Aug 20, 2019 · In this work, we propose a new unsupervised approach that learns to categorize entities into a hierarchy of named groups. We show that our approach is able to effectively learn entity groups using a scalable procedure in noisy and sparse datasets.

We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an un-known number of identities using a training set of images annotated with labels belonging to a disjoint set of identi-ties.

Jan 13, 2022 · Unsupervised Machine Learning for graphs can mainly be sectioned into these categories: Matrix Factorization, Skip-Gram, Autoencoders, and Graph Neural Networks. Graph Machine Learning (Claudio Stamile, Aldo Marzullo, Enrico Deusebio) has a fantastic image that outlines these and the algorithms beneath each:

Mar 12, 2020 · Clustering is a type of unsupervised machine learning technique used for grouping similar objects or data points into clusters. The goal of…

Apr 29, 2025 · The ability to learn from data by uncovering its underlying patterns and grouping it into distinct clusters based on latent similarities and differences is a central focus in machine learning and artificial intelligence. ... and an unsupervised (no labeled data) local clustering methods which outperform the state-of-the-arts local and non-local ...

In supervised learning historical input and output data is required to develop predictive models by making iterative predictions In unsupervised learning only input data is used to discover hidden

Apr 2, 2025 · Unsupervised graph representation learning focuses on generating meaningful node embeddings without relying on labeled data. This approach is crucial in scenarios where obtaining labels is expensive or impractical. The methods can be categorized into three main paradigms: generative, predictive, and contrastive learning.