15 graph pattern mining in data mining dm

okay, let's dive into the world of graph pattern mining. this will be a comprehensive tutorial covering the concepts, algorithms, and practical implementation of 15 common graph pattern mining techniques in data mining. we'll use python with libraries like `networkx` and `graphtools` to illustrate the code examples.

**tutorial: graph pattern mining in data mining**

**1. introduction to graph pattern mining**

graph pattern mining is a subfield of data mining that focuses on discovering interesting and recurring patterns within graph-structured data. unlike traditional data mining techniques that deal with tabular or unstructured text, graph pattern mining leverages the relationships (edges) between entities (nodes) to extract valuable knowledge.

**why is graph pattern mining important?**

* **rich representation:** graphs naturally represent complex relationships found in social networks, biological networks, knowledge graphs, web structures, and many other domains.
* **knowledge discovery:** by identifying frequent or significant subgraphs, we can uncover insights about the underlying structure and behavior of the graph.
* **prediction and classification:** graph patterns can be used as features for predicting node properties, classifying graphs, or recommending connections.
* **anomaly detection:** rare or unexpected subgraphs can indicate anomalous behavior or errors in the data.

**basic graph concepts:**

* **graph:** a data structure consisting of nodes (vertices) and edges (relationships between nodes). formally, g = (v, e), where v is the set of vertices and e is the set of edges.
* **directed graph:** edges have direction (e.g., a follows relationship on twitter).
* **undirected graph:** edges have no direction (e.g., a friendship relationship on facebook).
* **labeled graph:** nodes and/or edges have labels (e.g., a protein in a biological network with a label indicating its function).
* **subgraph:** a graph whose vertices and e ...

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graph pattern mining
data mining
graph algorithms
frequent subgraph mining
graph databases
pattern discovery
data visualization
network analysis
structural patterns
graph traversal
community detection
similarity measures
scalable algorithms
pattern matching
real-world applications