The objective of a community detection algorithm is to group
similar nodes in a network into communities, while increasing the dis-
similarity between them. Several methods have been proposed but many
of them are not suitable for large-scale networks because they have high
complexity and use global knowledge. The Label Propagation Algorithm
(LPA) assigns a unique label to every node and propagates the labels
locally, while applying the majority rule to reach a consensus. Nodes
which share the same label are then grouped into communities. Although
LPA excels with near linear execution time, it gets easily stuck in local
optima and often returns a single giant community. To overcome these
problems we propose MemLPA, a novel LPA where each node imple-
ments memory and the decision rule takes past states of the network
into account. We demonstrate through extensive experiments on the
Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world net-
works that MemLPA outperforms most of state-of-the-art community
detection algorithms.
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