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Graph Mining Techniques Tools and Issues

What is graph mining techniques?
Graph Mining is the set of tools and techniques used to (a) analyze the properties of real-world graphs, (b) predict how the structure and properties of a given graph might affect some application, and (c) develop models that can generate realistic graphs that match the patterns found in real-world graphs of interest.
What are the four 4 main data mining techniques?
Data mining typically uses four techniques to create descriptive and predictive power: regression, association rule discovery, classification and clustering.
Regression Analysis. Association Rule Discovery. Classification. Clustering.
What is data mining tools and techniques?
Data Mining tools are software programs that help in framing and executing data mining techniques to create data models and test them as well.
It is usually a framework like R studio or Tableau with a suite of programs to help build and test a data model.
The graph is used in data mining to discover subgraph patterns for discrimination, classification, data clustering, etc.
Graph analysis is a process that uses it.
Graphs can be used to create networks like the internet, computer networks, social networks, etc. by connecting the different nodes.

What is graph mining techniques?