1 research outputs found
An ensemble based on a bi-objective evolutionary spectral algorithm for graph clustering
Graph clustering is a challenging pattern recognition problem whose goal is
to identify vertex partitions with high intra-group connectivity. This paper
investigates a bi-objective problem that maximizes the number of intra-cluster
edges of a graph and minimizes the expected number of inter-cluster edges in a
random graph with the same degree sequence as the original one. The difference
between the two investigated objectives is the definition of the well-known
measure of graph clustering quality: the modularity. We introduce a spectral
decomposition hybridized with an evolutionary heuristic, called MOSpecG, to
approach this bi-objective problem and an ensemble strategy to consolidate the
solutions found by MOSpecG into a final robust partition. The results of
computational experiments with real and artificial LFR networks demonstrated a
significant improvement in the results and performance of the introduced method
in regard to another bi-objective algorithm found in the literature. The
crossover operator based on the geometric interpretation of the modularity
maximization problem to match the communities of a pair of individuals was of
utmost importance for the good performance of MOSpecG. Hybridizing spectral
graph theory and intelligent systems allowed us to define significantly
high-quality community structures.Comment: Preprint accepted for publication in Expert Systems with Application