Extremal Ensemble Learning (EEL) is a
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
algorithmic paradigm for
graph partitioning. EEL creates an
ensemble of partitions and then uses information contained in the ensemble to find new and improved partitions. The ensemble evolves and learns how to form improved partitions through
extremal updating procedure. The final solution is found by achieving
consensus among its member partitions about what the optimal partition is.
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Reduced Network Extremal Ensemble Learning (RenEEL)
A particular implementation of the EEL paradigm is the Reduced Network Extremal Ensemble Learning (RenEEL) scheme for partitioning a graph.
RenEEL uses consensus across many partitions in an ensemble to create a reduced network that can be efficiently analyzed to find more accurate partitions. These better quality partitions are subsequently used to update the ensemble. An algorithm that utilizes the RenEEL scheme is currently the best algorithm for finding the graph partition with maximum
modularity
Broadly speaking, modularity is the degree to which a system's components may be separated and recombined, often with the benefit of flexibility and variety in use. The concept of modularity is used primarily to reduce complexity by breaking a s ...
, which is an
NP-hard problem
In computational complexity theory, NP-hardness ( non-deterministic polynomial-time hardness) is the defining property of a class of problems that are informally "at least as hard as the hardest problems in NP". A simple example of an NP-hard pr ...
.
References
Machine learning algorithms
Graph algorithms
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