86,816 research outputs found
Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks
Nowadays, identification and detection community structures in complex
networks is an important factor in extracting useful information from networks.
Label propagation algorithm with near linear-time complexity is one of the most
popular methods for detecting community structures, yet its uncertainty and
randomness is a defective factor. Merging LPA with other community detection
metrics would improve its accuracy and reduce instability of LPA. Considering
this point, in this paper we tried to use edge betweenness centrality to
improve LPA performance. On the other hand, calculating edge betweenness
centrality is expensive, so as an alternative metric, we try to use local edge
betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge
Betweenness). Experimental results on both real-world and benchmark networks
show that LPA-LEB possesses higher accuracy and stability than LPA when
detecting community structures in networks.Comment: 6 page
Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks
Even as we advance the frontiers of physics knowledge, our understanding of
how this knowledge evolves remains at the descriptive levels of Popper and
Kuhn. Using the APS publications data sets, we ask in this letter how new
knowledge is built upon old knowledge. We do so by constructing year-to-year
bibliographic coupling networks, and identify in them validated communities
that represent different research fields. We then visualize their evolutionary
relationships in the form of alluvial diagrams, and show how they remain intact
through APS journal splits. Quantitatively, we see that most fields undergo
weak Popperian mixing, and it is rare for a field to remain isolated/undergo
strong mixing. The sizes of fields obey a simple linear growth with
recombination. We can also reliably predict the merging between two fields, but
not for the considerably more complex splitting. Finally, we report a case
study of two fields that underwent repeated merging and splitting around 1995,
and how these Kuhnian events are correlated with breakthroughs on BEC, quantum
teleportation, and slow light. This impact showed up quantitatively in the
citations of the BEC field as a larger proportion of references from during and
shortly after these events.Comment: 14 pages, 14 figures, 1 tabl
Complex networks created by aggregation
We study aggregation as a mechanism for the creation of complex networks. In
this evolution process vertices merge together, which increases the number of
highly connected hubs. We study a range of complex network architectures
produced by the aggregation. Fat-tailed (in particular, scale-free)
distributions of connections are obtained both for networks with a finite
number of vertices and growing networks. We observe a strong variation of a
network structure with growing density of connections and find the phase
transition of the condensation of edges. Finally, we demonstrate the importance
of structural correlations in these networks.Comment: 12 pages, 13 figure
Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems
Summary 1. Significant advances in both mathematical and molecular approaches in ecology offer unprecedented opportunities to describe and understand ecosystem functioning. Ecological networks describe interactions between species, the underlying structure of communities and the function and stability of ecosystems. They provide the ability to assess the robustness of complex ecological communities to species loss, as well as a novel way of guiding restoration. However, empirically quantifying the interactions between entire communities remains a significant challenge. 2. Concomitantly, advances in DNA sequencing technologies are resolving previously intractable questions in functional and taxonomic biodiversity and provide enormous potential to determine hitherto difficult to observe species interactions. Combining DNA metabarcoding approaches with ecological network analysis presents important new opportunities for understanding large-scale ecological and evolutionary processes, as well as providing powerful tools for building ecosystems that are resilient to environmental change. 3. We propose a novel ânested taggingâ metabarcoding approach for the rapid construction of large, phylogenetically structured species-interaction networks. Taking treeâinsectâparasitoid ecological networks as an illustration, we show how measures of network robustness, constructed using DNA metabarcoding, can be used to determine the consequences of tree species loss within forests, and forest habitat loss within wider landscapes. By determining which species and habitats are important to network integrity, we propose new directions for forest management. 4. Merging metabarcoding with ecological network analysis provides a revolutionary opportunity to construct some of the largest, phylogenetically structured species-interaction networks to date, providing new ways to: (i) monitor biodiversity and ecosystem functioning; (ii) assess the robustness of interacting communities to species loss; and (iii) build ecosystems that are more resilient to environmental change
Targeted Community Merging provides an efficient comparison between collaboration clusters and departmental partitions
Community detection theory is vital for the structural analysis of many types of complex networks, especially for human-like collaboration networks. In this work, we present a new community detection algorithm, the Targeted Community Merging algorithm, based on the well-known GirvanâNewman algorithm, which allows obtaining community partitions with high values of modularity and a small number of communities. We then perform an analysis and comparison between the departmental and community structure of scientific collaboration networks within the University of Zaragoza. Thus, we draw valuable conclusions from the inter- and intra-departmental collaboration structure that could be useful to take decisions on an eventual departmental restructuring
Backbone of credit relationships in the Japanese credit market
We detect the backbone of the weighted bipartite network of the Japanese
credit market relationships. The backbone is detected by adapting a general
method used in the investigation of weighted networks. With this approach we
detect a backbone that is statistically validated against a null hypothesis of
uniform diversification of loans for banks and firms. Our investigation is done
year by year and it covers more than thirty years during the period from 1980
to 2011. We relate some of our findings with economic events that have
characterized the Japanese credit market during the last years. The study of
the time evolution of the backbone allows us to detect changes occurred in
network size, fraction of credit explained, and attributes characterizing the
banks and the firms present in the backbone.Comment: 14 pages, 8 figure
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