6 research outputs found
A Quorum Sensing Inspired Algorithm for Dynamic Clustering
Quorum sensing is a decentralized biological process, through which a
community of cells with no global awareness coordinate their functional
behaviors based solely on cell-medium interactions and local decisions. This
paper draws inspirations from quorum sensing and colony competition to derive a
new algorithm for data clustering. The algorithm treats each data as a single
cell, and uses knowledge of local connectivity to cluster cells into multiple
colonies simultaneously. It simulates auto-inducers secretion in quorum sensing
to tune the influence radius for each cell. At the same time, sparsely
distributed core cells spread their influences to form colonies, and
interactions between colonies eventually determine each cell's identity. The
algorithm has the flexibility to analyze not only static but also time-varying
data, which surpasses the capacity of many existing algorithms. Its stability
and convergence properties are established. The algorithm is tested on several
applications, including both synthetic and real benchmarks data sets, alleles
clustering, community detection, image segmentation. In particular, the
algorithm's distinctive capability to deal with time-varying data allows us to
experiment it on novel applications such as robotic swarms grouping and
switching model identification. We believe that the algorithm's promising
performance would stimulate many more exciting applications
Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
Background: Network communities help the functional organization and
evolution of complex networks. However, the development of a method, which is
both fast and accurate, provides modular overlaps and partitions of a
heterogeneous network, has proven to be rather difficult. Methodology/Principal
Findings: Here we introduce the novel concept of ModuLand, an integrative
method family determining overlapping network modules as hills of an influence
function-based, centrality-type community landscape, and including several
widely used modularization methods as special cases. As various adaptations of
the method family, we developed several algorithms, which provide an efficient
analysis of weighted and directed networks, and (1) determine pervasively
overlapping modules with high resolution; (2) uncover a detailed hierarchical
network structure allowing an efficient, zoom-in analysis of large networks;
(3) allow the determination of key network nodes and (4) help to predict
network dynamics. Conclusions/Significance: The concept opens a wide range of
possibilities to develop new approaches and applications including network
routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information
containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18
module definitions, 129 different modularization methods, 13 module
comparision methods) and 396 references. All algorithms can be downloaded
from this web-site: http://www.linkgroup.hu/modules.ph