2 research outputs found
Detecting modules in dense weighted networks with the Potts method
We address the problem of multiresolution module detection in dense weighted
networks, where the modular structure is encoded in the weights rather than
topology. We discuss a weighted version of the q-state Potts method, which was
originally introduced by Reichardt and Bornholdt. This weighted method can be
directly applied to dense networks. We discuss the dependence of the resolution
of the method on its tuning parameter and network properties, using sparse and
dense weighted networks with built-in modules as example cases. Finally, we
apply the method to data on stock price correlations, and show that the
resulting modules correspond well to known structural properties of this
correlation network.Comment: 14 pages, 6 figures. v2: 1 figure added, 1 reference added, minor
changes. v3: 3 references added, minor change
Improved community structure detection using a modified fine tuning strategy
The community structure of a complex network can be determined by finding the
partitioning of its nodes that maximizes modularity. Many of the proposed
algorithms for doing this work by recursively bisecting the network. We show
that this unduely constrains their results, leading to a bias in the size of
the communities they find and limiting their effectivness. To solve this
problem, we propose adding a step to the existing algorithms that does not
increase the order of their computational complexity. We show that, if this
step is combined with a commonly used method, the identified constraint and
resulting bias are removed, and its ability to find the optimal partitioning is
improved. The effectiveness of this combined algorithm is also demonstrated by
using it on real-world example networks. For a number of these examples, it
achieves the best results of any known algorithm.Comment: 6 pages, 3 figures, 1 tabl