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2018 ; 8
(1
): 10892
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Compressing Networks with Super Nodes
#MMPMID30022035
Stanley N
; Kwitt R
; Niethammer M
; Mucha PJ
Sci Rep
2018[Jul]; 8
(1
): 10892
PMID30022035
show ga
Community detection is a commonly used technique for identifying groups in a
network based on similarities in connectivity patterns. To facilitate community
detection in large networks, we recast the network as a smaller network of 'super
nodes', where each super node comprises one or more nodes of the original
network. We can then use this super node representation as the input into
standard community detection algorithms. To define the seeds, or centers, of our
super nodes, we apply the 'CoreHD' ranking, a technique applied in network
dismantling and decycling problems. We test our approach through the analysis of
two common methods for community detection: modularity maximization with the
Louvain algorithm and maximum likelihood optimization for fitting a stochastic
block model. Our results highlight that applying community detection to the
compressed network of super nodes is significantly faster while successfully
producing partitions that are more aligned with the local network connectivity
and more stable across multiple (stochastic) runs within and between community
detection algorithms, yet still overlap well with the results obtained using the
full network.
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