On bicliques and the second clique graph of suspensions

2020 ◽  
Vol 281 ◽  
pp. 261-267
Author(s):  
M.A. Pizaña ◽  
I.A. Robles
Keyword(s):  
2016 ◽  
Vol 4 ◽  
pp. 791-795
Author(s):  
Shinta Koyano ◽  
Lukáš Pichl

Population dynamics in the evolution, extinction, and re-evolution of various logic-function performing organisms is studied in the artificial life system, Avida. Following the work of Yedid (2009), we design an experiment involving two extinction regimes, pulse-extinction (corresponding to a random-kill event) and press-extinction (corresponding to a prolonged episode of rare resources). In addition, we study the effect of environmental topology (toroidal grid and clique graph). In the study of population dynamics, logarithmic returns are generally applied. The resulting distributions display a fat tail form of the power law: the more complex the logic function (in terms of NAND components), the broader the full width at half a maximum of the histogram. The power law exponents were in sound agreement with those of “real-life” populations and distributions. The distributions of evolutionary times, as well as post-extinction recovery periods, were very broad, and presumably had no standard deviations. Using 100 runs of 200,000 updates for each of the four cases (about 1 month of central processing unit time), we established the dynamics of the average population, with the effect of world topology.


2015 ◽  
Vol 485 ◽  
pp. 33-46 ◽  
Author(s):  
Yaoping Hou ◽  
Aixiang Fang ◽  
Yajing Sun
Keyword(s):  

Author(s):  
G. H. Shirdel ◽  
B. Vaez-Zadeh

A hypergraph is given by [Formula: see text], where [Formula: see text] is a set of vertices and [Formula: see text] is a set of nonempty subsets of [Formula: see text], the member of [Formula: see text] is named hyperedge. So, a hypergraph is a nature generalization of a graph. A hypergraph has a complex structure, thus some researchers try to transform a hypergraph to a graph. In this paper, we define two graphs, Clique graph and Persian graph. These relations are one to one. We can find the shortest path between two vertices in a hypergraph [Formula: see text], by using the Dijkstra algorithm in graph theory on the graphs corresponding to [Formula: see text].


2019 ◽  
Vol 29 (6) ◽  
pp. 1619-1630 ◽  
Author(s):  
Wei-Zhi Nie ◽  
An-An Liu ◽  
Yue Gao ◽  
Yu-Ting Su
Keyword(s):  

Author(s):  
L. Alcón ◽  
L. Faria ◽  
C. M. H. de Figueiredo ◽  
M. Gutierrez

2009 ◽  
Vol 30 (1) ◽  
pp. 288-294 ◽  
Author(s):  
F. Larrión ◽  
M.A. Pizaña ◽  
R. Villarroel-Flores

Author(s):  
Liliana Alcón ◽  
Luerbio Faria ◽  
Celina M. H. de Figueiredo ◽  
Marisa Gutierrez
Keyword(s):  

2016 ◽  
Vol 25 (5) ◽  
pp. 2103-2116 ◽  
Author(s):  
An-An Liu ◽  
Wei-Zhi Nie ◽  
Yue Gao ◽  
Yu-Ting Su

2017 ◽  
Vol 29 (6) ◽  
pp. 1681-1695 ◽  
Author(s):  
Asieh Abolpour Mofrad ◽  
Matthew G. Parker

Clique-based neural associative memories introduced by Gripon and Berrou (GB), have been shown to have good performance, and in our previous work we improved the learning capacity and retrieval rate by local coding and precoding in the presence of partial erasures. We now take a step forward and consider nested-clique graph structures for the network. The GB model stores patterns as small cliques, and we here replace these by nested cliques. Simulation results show that the nested-clique structure enhances the clique-based model.


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