44,794 research outputs found
Hidden geometries in networks arising from cooperative self-assembly
Multilevel self-assembly involving small structured groups of nano-particles
provides new routes to development of functional materials with a sophisticated
architecture. Apart from the inter-particle forces, the geometrical shapes and
compatibility of the building blocks are decisive factors in each phase of
growth. Therefore, a comprehensive understanding of these processes is
essential for the design of large assemblies of desired properties. Here, we
introduce a computational model for cooperative self-assembly with simultaneous
attachment of structured groups of particles, which can be described by
simplexes (connected pairs, triangles, tetrahedrons and higher order cliques)
to a growing network, starting from a small seed. The model incorporates
geometric rules that provide suitable nesting spaces for the new group and the
chemical affinity of the system to accepting an excess number of
particles. For varying chemical affinity, we grow different classes of
assemblies by binding the cliques of distributed sizes. Furthermore, to
characterise the emergent large-scale structures, we use the metrics of graph
theory and algebraic topology of graphs, and 4-point test for the intrinsic
hyperbolicity of the networks. Our results show that higher Q-connectedness of
the appearing simplicial complexes can arise due to only geometrical factors,
i.e., for , and that it can be effectively modulated by changing the
chemical potential and the polydispersity of the size of binding simplexes. For
certain parameters in the model we obtain networks of mono-dispersed clicks,
triangles and tetrahedrons, which represent the geometrical descriptors that
are relevant in quantum physics and frequently occurring chemical clusters.Comment: 9 pages, 8 figure
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.Comment: To appear in Journal of Computer and System Science
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding
different relationships and their interactions in a complex system, where each
layer in a multilayer network depicts the topological structure of a group of
nodes corresponding to a particular relationship. The interactions among
different layers imply how the interplay of different relations on the topology
of each layer. For a single-layer network, network embedding methods have been
proposed to project the nodes in a network into a continuous vector space with
a relatively small number of dimensions, where the space embeds the social
representations among nodes. These algorithms have been proved to have a better
performance on a variety of regular graph analysis tasks, such as link
prediction, or multi-label classification. In this paper, by extending a
standard graph mining into multilayer network, we have proposed three methods
("network aggregation," "results aggregation" and "layer co-analysis") to
project a multilayer network into a continuous vector space. From the
evaluation, we have proved that comparing with regular link prediction methods,
"layer co-analysis" achieved the best performance on most of the datasets,
while "network aggregation" and "results aggregation" also have better
performance than regular link prediction methods
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