15,161 research outputs found
Intrinsically Dynamic Network Communities
Community finding algorithms for networks have recently been extended to
dynamic data. Most of these recent methods aim at exhibiting community
partitions from successive graph snapshots and thereafter connecting or
smoothing these partitions using clever time-dependent features and sampling
techniques. These approaches are nonetheless achieving longitudinal rather than
dynamic community detection. We assume that communities are fundamentally
defined by the repetition of interactions among a set of nodes over time.
According to this definition, analyzing the data by considering successive
snapshots induces a significant loss of information: we suggest that it blurs
essentially dynamic phenomena - such as communities based on repeated
inter-temporal interactions, nodes switching from a community to another across
time, or the possibility that a community survives while its members are being
integrally replaced over a longer time period. We propose a formalism which
aims at tackling this issue in the context of time-directed datasets (such as
citation networks), and present several illustrations on both empirical and
synthetic dynamic networks. We eventually introduce intrinsically dynamic
metrics to qualify temporal community structure and emphasize their possible
role as an estimator of the quality of the community detection - taking into
account the fact that various empirical contexts may call for distinct
`community' definitions and detection criteria.Comment: 27 pages, 11 figure
Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids
Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively)
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Despite their claimed biological plausibility, most self organizing networks
have strict topological constraints and consequently they cannot take into
account a wide range of external stimuli. Furthermore their evolution is
conditioned by deterministic laws which often are not correlated with the
structural parameters and the global status of the network, as it should happen
in a real biological system. In nature the environmental inputs are noise
affected and fuzzy. Which thing sets the problem to investigate the possibility
of emergent behaviour in a not strictly constrained net and subjected to
different inputs. It is here presented a new model of Evolutionary Neural Gas
(ENG) with any topological constraints, trained by probabilistic laws depending
on the local distortion errors and the network dimension. The network is
considered as a population of nodes that coexist in an ecosystem sharing local
and global resources. Those particular features allow the network to quickly
adapt to the environment, according to its dimensions. The ENG model analysis
shows that the net evolves as a scale-free graph, and justifies in a deeply
physical sense- the term gas here used.Comment: 16 pages, 8 figure
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