Modularity Optimization
Glossary
- Directed
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Directed trait. The algorithm is well-defined on a directed graph.
- Directed
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Directed trait. The algorithm ignores the direction of the graph.
- Directed
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Directed trait. The algorithm does not run on a directed graph.
- Undirected
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Undirected trait. The algorithm is well-defined on an undirected graph.
- Undirected
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Undirected trait. The algorithm ignores the undirectedness of the graph.
- Heterogeneous nodes
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Heterogeneous nodes fully supported. The algorithm has the ability to distinguish between nodes of different types.
- Heterogeneous nodes
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Heterogeneous nodes allowed. The algorithm treats all selected nodes similarly regardless of their label.
- Heterogeneous relationships
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Heterogeneous relationships fully supported. The algorithm has the ability to distinguish between relationships of different types.
- Heterogeneous relationships
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Heterogeneous relationships allowed. The algorithm treats all selected relationships similarly regardless of their type.
- Weighted relationships
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Weighted trait. The algorithm supports a relationship property to be used as weight, specified via the relationshipWeightProperty configuration parameter.
- Weighted relationships
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Weighted trait. The algorithm treats each relationship as equally important, discarding the value of any relationship weight.
- Node properties
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Node properties trait. The algorithm makes use of node properties.
Introduction
The Modularity Optimization algorithm tries to detect communities in the graph based on their modularity. Modularity is a measure of the structure of a graph, measuring the density of connections within a module or community. Graphs with a high modularity score will have many connections within a community but only few pointing outwards to other communities. The algorithm will explore for every node if its modularity score might increase if it changes its community to one of its neighboring nodes.
For more information on this algorithm, see:
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Running this algorithm requires sufficient memory availability. Before running this algorithm, we recommend that you read Memory Estimation. |