Semi-supervised learning

As explained in Knowledge Graphs and LLMs in Action

Semi-supervised learning is a machine-learning paradigm that learns from a combination of labeled and unlabeled examples.

Semi-supervised learning combines labeled and unlabeled examples during training. In graph problems, unlabeled nodes can contribute neighborhood and structural information even when their labels remain unknown. section 9.2.1

The approach is especially relevant to node classification and link prediction, where the full graph is available but labels are missing for some nodes. Classical formulations assume independent, identically distributed data; graph dependencies violate that assumption. section 9.2.1 section 9.4

Predicting Flickr interests

Flickr user connections form a graph, while interest-group memberships provide labels. Multilabel node classification can predict groups that users may join but have not yet joined. section 9.2.1

Figure 9.2 The typical flow of node classification. Like many supervised ML tasks, it has two phases: training and prediction (which, in this case, translates into classifying unclassified nodes).

Use the node-classification flow to distinguish training from prediction over labeled and unclassified nodes. section 9.2.1

How Knowledge Graphs and LLMs in Action covers it

How deep it goes

Defines the paradigm and its graph-specific limitation but stops short of algorithmic implementation.

Examples

Uses Flickr users, social connections, interest groups, and a node-classification training-prediction flow.

What you can do after

Evaluate when unlabeled graph nodes can provide useful structural information despite missing labels.

How it connects

Connects node classification and link prediction with graph structure, node embeddings, and multilabel prediction.

liveBookmanning.com

Read Knowledge Graphs and LLMs in Action on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Outlier Detection in PythonSemisupervised learning is a machine-learning paradigm that trains with labeled data while also exploiting substantially larger amounts of unlabeled data.
• 

  liveBookmanning.com
  Financial AI in PracticeSemi-supervised learning uses a limited amount of labeled data together with a larger pool of unlabeled data.
• 

  liveBookmanning.com
  Causal AISemi-supervision is a learning setting that combines labeled and unlabeled examples to train a model.
• 

  liveBookmanning.com
  Graph Neural Networks in ActionSemi-supervised learning is a learning setting that combines labeled and unlabeled examples during model development.

Compare how 5 books explain semi-supervised learning →

Related concepts

• unsupervised learninguses · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
• supervised learninguses · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
• labeled dataused by · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
• node classificationused by · Node classification is a graph-learning task that predicts one or more discrete category labels for individual nodes in a graph.
• link predictionused by · Link prediction is a graph-learning task that estimates whether a connection exists or may occur between nodes, often by scoring candidate links.

This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.