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The topology of the directed clique complex as a network invariant

Overview of attention for article published in arXiv, March 2016
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  • High Attention Score compared to outputs of the same age and source (89th percentile)

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Title
The topology of the directed clique complex as a network invariant
Published in
arXiv, March 2016
DOI 10.1186/s40064-016-2022-y
Pubmed ID
Authors

Paolo Masulli, Alessandro E. P. Villa

Abstract

We introduce new algebro-topological invariants of directed networks, based on the topological construction of the directed clique complex. The shape of the underlying directed graph is encoded in a way that can be studied mathematically to obtain network invariants such as the Euler characteristic and the Betti numbers. Two different cases illustrate the application of the Euler characteristic. We investigate how the evolution of a Boolean recurrent artificial neural network is influenced by its topology in a dynamics involving pruning and strengthening of the connections, and to show that the topological features of the directed clique complex influence the dynamical evolution of the network. The second application considers the directed clique complex in a broader framework, to define an invariant of directed networks, the network degree invariant, which is constructed by computing the topological invariant on a sequence of sub-networks filtered by the minimum in- or out-degree of the nodes. The application of the Euler characteristic presented here can be extended to any directed network and provides a new method for the assessment of specific functional features associated with the network topology.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 30%
Student > Master 4 11%
Student > Bachelor 3 8%
Other 3 8%
Professor > Associate Professor 2 5%
Other 5 14%
Unknown 9 24%
Readers by discipline Count As %
Mathematics 9 24%
Engineering 6 16%
Neuroscience 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Computer Science 2 5%
Other 5 14%
Unknown 10 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 April 2016.
All research outputs
#6,556,178
of 25,617,409 outputs
Outputs from arXiv
#105,233
of 932,197 outputs
Outputs of similar age
#87,001
of 315,953 outputs
Outputs of similar age from arXiv
#1,249
of 12,477 outputs
Altmetric has tracked 25,617,409 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 932,197 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 315,953 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 12,477 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.