  Modularity maximization is one of the state-of-the-art methods for community
detection that has gained popularity in the last decade. Yet it suffers from
the resolution limit problem by preferring under certain conditions large
communities over small ones. To solve this problem, we propose to expand the
meaning of the edges that are currently used to indicate propensity of nodes
for sharing the same community. In our approach this is the role of edges with
positive weights while edges with negative weights indicate aversion for
putting their end-nodes into one community. We also present a novel regression
model which assigns weights to the edges of a graph according to their local
topological features to enhance the accuracy of modularity maximization
algorithms. We construct artificial graphs based on the parameters sampled from
a given unweighted network and train the regression model on ground truth
communities of these artificial graphs in a supervised fashion. The extraction
of local topological edge features can be done in linear time, making this
process efficient. Experimental results on real and synthetic networks show
that the state-of-the-art community detection algorithms improve their
performance significantly by finding communities in the weighted graphs
produced by our model.
