Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reduced network extremal ensemble learning (RenEEL) scheme for community
  detection in complex networks   We introduce an ensemble learning scheme for community detection in complex
networks. The scheme uses a Machine Learning algorithmic paradigm we call
Extremal Ensemble Learning. It uses iterative extremal updating of an ensemble
of network partitions, which can be found by a conventional base algorithm, to
find a node partition that maximizes modularity. At each iteration, core groups
of nodes that are in the same community in every ensemble partition are
identified and used to form a reduced network. Partitions of the reduced
network are then found and used to update the ensemble. The smaller size of the
reduced network makes the scheme efficient. We use the scheme to analyze the
community structure in a set of commonly studied benchmark networks and find
that it outperforms all other known methods for finding the partition with
maximum modularity.

15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an