Graph machine learning

As explained in Graph Algorithms for Data Science

Graph machine learning is machine learning that represents data and their relationships as a graph and exploits graph structure during modeling.

Graph machine learning applies machine learning to data represented as graphs, using relationships and network position to add context to individual data points and improve modeling. section 1.1

Common graph machine learning workflows include:

• Node classification and regression, augmented with features describing a data point’s role or position in the network. section 1.1
• Clustering, using graph metrics that encode the role or position of each data point. section 1.1
• Link prediction, for business scenarios that require predicting future relationships. section 1.1
• Marketing and influence analysis, using important or well-positioned nodes to guide information-spreading strategies. section 1.1

Social-network analysis

Represent a social network as a graph, identify influential nodes or community structure, and feed those network signals into classification, clustering, or link-prediction workflows. section 1.1

Figure 1.10 Graph-based machine learning

Use the graph-based machine learning figure to connect graph structure with downstream modeling workflows. section 1.1

How Graph Algorithms for Data Science covers it

How deep it goes

Explains workflow applications and graph-derived features, while stopping short of detailed machine-learning implementation.

Examples

Uses social-platform graphs, influential nodes, community structure, and future-link prediction as concrete examples.

What it assumes

Assumes a graph representation and builds toward machine learning through graph modeling, querying, and algorithms.

What you can do after

Evaluate graph-shaped machine-learning problems and select node, clustering, or link-prediction workflows using network context.

How it connects

Connects graph modeling, graph query language, graph algorithms, community structure, node influence, classification, regression, clustering, and link prediction.

liveBookmanning.com

Read Graph Algorithms for Data Science on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Optimization Algorithms: AI techniques for design, planning, and control problemsGraph machine learning is machine learning on graph-structured data that models relationships among entities as well as entity attributes.
• 

  liveBookmanning.com
  Graph Neural Networks in ActionGraph-based learning applies machine learning to data represented as a graph and learns from its structural relationships.

Compare how 3 books explain graph machine learning →

Related concepts

• machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
• graph neural networkused by · A graph neural network is a neural-network model that learns representations or predictions from graph-structured data by combining information from connected entities.
• embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
• deep learningkind of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• permutation invarianceuses · Permutation invariance is the property that a computation’s result remains unchanged when equivalent input elements are reordered.
• local neighborhooduses · A local neighborhood is a set of elements considered near a specified element according to a defined spatial, topological, distance-based, or relational criterion.

This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.