121 research outputs found
Community detection and graph partitioning
Many methods have been proposed for community detection in networks. Some of
the most promising are methods based on statistical inference, which rest on
solid mathematical foundations and return excellent results in practice. In
this paper we show that two of the most widely used inference methods can be
mapped directly onto versions of the standard minimum-cut graph partitioning
problem, which allows us to apply any of the many well-understood partitioning
algorithms to the solution of community detection problems. We illustrate the
approach by adapting the Laplacian spectral partitioning method to perform
community inference, testing the resulting algorithm on a range of examples,
including computer-generated and real-world networks. Both the quality of the
results and the running time rival the best previous methods.Comment: 5 pages, 2 figure
A network approach for power grid robustness against cascading failures
Cascading failures are one of the main reasons for blackouts in electrical
power grids. Stable power supply requires a robust design of the power grid
topology. Currently, the impact of the grid structure on the grid robustness is
mainly assessed by purely topological metrics, that fail to capture the
fundamental properties of the electrical power grids such as power flow
allocation according to Kirchhoff's laws. This paper deploys the effective
graph resistance as a metric to relate the topology of a grid to its robustness
against cascading failures. Specifically, the effective graph resistance is
deployed as a metric for network expansions (by means of transmission line
additions) of an existing power grid. Four strategies based on network
properties are investigated to optimize the effective graph resistance,
accordingly to improve the robustness, of a given power grid at a low
computational complexity. Experimental results suggest the existence of
Braess's paradox in power grids: bringing an additional line into the system
occasionally results in decrease of the grid robustness. This paper further
investigates the impact of the topology on the Braess's paradox, and identifies
specific sub-structures whose existence results in Braess's paradox. Careful
assessment of the design and expansion choices of grid topologies incorporating
the insights provided by this paper optimizes the robustness of a power grid,
while avoiding the Braess's paradox in the system.Comment: 7 pages, 13 figures conferenc
Inference of hidden structures in complex physical systems by multi-scale clustering
We survey the application of a relatively new branch of statistical
physics--"community detection"-- to data mining. In particular, we focus on the
diagnosis of materials and automated image segmentation. Community detection
describes the quest of partitioning a complex system involving many elements
into optimally decoupled subsets or communities of such elements. We review a
multiresolution variant which is used to ascertain structures at different
spatial and temporal scales. Significant patterns are obtained by examining the
correlations between different independent solvers. Similar to other
combinatorial optimization problems in the NP complexity class, community
detection exhibits several phases. Typically, illuminating orders are revealed
by choosing parameters that lead to extremal information theory correlations.Comment: 25 pages, 16 Figures; a review of earlier work
Modularity bounds for clusters located by leading eigenvectors of the normalized modularity matrix
Nodal theorems for generalized modularity matrices ensure that the cluster
located by the positive entries of the leading eigenvector of various
modularity matrices induces a connected subgraph. In this paper we obtain lower
bounds for the modularity of that set of nodes showing that, under certain
conditions, the nodal domains induced by eigenvectors corresponding to highly
positive eigenvalues of the normalized modularity matrix have indeed positive
modularity, that is they can be recognized as modules inside the network.
Moreover we establish Cheeger-type inequalities for the cut-modularity of the
graph, providing a theoretical support to the common understanding that highly
positive eigenvalues of modularity matrices are related with the possibility of
subdividing a network into communities
- …