Apr 4, 2023 · Graph Machine Learning (GML) is a broad field with many use case applications and comprising multiple different supervised and unsupervised ML tasks; One of the primary purposes of GML is compressing large sparse graph structures while maintaining important signals for prediction and inference.

Mar 25, 2024 · Unsupervised learning is a type of machine learning where the algorithm is provided with input data without explicit instructions on what to do with it. The system tries to learn the patterns and structure inherent in the data without labeled outputs.

Feb 27, 2025 · Supervised and unsupervised learning are two key approaches in machine learning. In supervised learning, the model is trained with labeled data where each input is paired with a corresponding output.

Mar 12, 2021 · Within artificial intelligence (AI) and machine learning, there are two basic approaches: supervised learning and unsupervised learning. The main difference is that one uses labeled data to help predict outcomes, while the other does not.

Jan 13, 2022 · Unsupervised Embeddings on Graphs. 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:

Feb 13, 2025 · In this tutorial, we’ll discuss some real-life examples of supervised and unsupervised learning. 2. Definitions. In supervised learning, we aim to train a model to be capable of mapping an input to output after learning some features, acquiring a generalization ability to correctly classify never-seen samples of data.

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).

In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised methods. Unsupervised learning algorithms usually aim at visualising graphs in latent spaces and/or clustering the nodes. Both focus on extracting knowledge from graph topologies.

Apr 25, 2022 · In this part, I covered how you can take graph information to conduct Supervised and Semi-Supervised learning. The value of using graphs provides rich spatial connectivity and centrality features at the minimum and a wide array of new techniques to expand your repertoire of problem-solving strategies.

Apr 15, 2025 · Supervised and unsupervised learning are foundational pillars of machine learning, each serving distinct purposes and offering unique advantages. Supervised learning is highly effective for prediction and classification tasks that require labeled data, while unsupervised learning excels at uncovering patterns and conducting exploratory data ...