scholarly journals Feedback through graph motifs relates structure and function in complex networks

2018 ◽  
Vol 98 (6) ◽  
Author(s):  
Yu Hu ◽  
Steven L. Brunton ◽  
Nicholas Cain ◽  
Stefan Mihalas ◽  
J. Nathan Kutz ◽  
...  
Keyword(s):  
Complex Networks ◽  
Structure And Function ◽  
And Function

scholarly journals A tensor-based framework for studying eigenvector multicentrality in multilayer networks

2019 ◽  
Vol 116 (31) ◽  
pp. 15407-15413 ◽  
Author(s):  
Mincheng Wu ◽  
Shibo He ◽  
Yongtao Zhang ◽  
Jiming Chen ◽  
Youxian Sun ◽  
...  
Keyword(s):  
Complex Networks ◽  
Prior Knowledge ◽  
General Framework ◽  
Single Layer ◽  
Structure And Function ◽  
Centrality Measures ◽  
Multilayer Networks ◽  
And Function ◽  
The Impact ◽  
Insight Into

Centrality is widely recognized as one of the most critical measures to provide insight into the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.

scholarly journals The Structure and Function of Complex Networks

SIAM Review ◽  
2003 ◽  
Vol 45 (2) ◽  
pp. 167-256 ◽  
Author(s):  
M. E. J. Newman
Keyword(s):  
Complex Networks ◽  
Structure And Function ◽  
And Function

scholarly journals Hunting for Influential Nodes in Complex Networks Using Local Information

2021 ◽  
Author(s):  
Zhihao Dong ◽  
Yuanzhu Chen ◽  
Terrence S. Tricco ◽  
Cheng Li ◽  
Ting Hu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Structure And Function ◽  
Epidemic Threshold ◽  
Global Knowledge ◽  
The Real ◽  
Influential Nodes ◽  
Influential Node ◽  
And Function ◽  
High Degree

Abstract Complex networks in the real world are often with heterogeneous degree distributions. The structure and function of nodes can vary significantly, with influential nodes playing a crucial role in information spread and other spreading phenomena. Identifying high-degree nodes enables change to the network’s structure and function. Previous work either redefines metrics used to measure the nodes’ importance or focus on developing algorithms to efficiently find influential nodes. These approaches typically rely on global knowledge of the network and assume that the structure of the network does not change over time, both of which are difficult to achieve in the real world. In this paper, we propose a decentralized strategy that can find influential nodes without global knowledge of the network. Our Joint Nomination (JN) strategy selects a random set of nodes along with a set of nodes connected to those nodes, and together they nominate the influential node set. Experiments are conducted on 12 network datasets, including both synthetic and real-world networks, both undirected and directed networks. Results show that average degree of the identified node set is about 3–8 times higher than that of the full node set, and the degree distribution skews toward higher-degree nodes. Removal of influential nodes increase the average shortest path length by 20–70% over the original network, or about 8–15% longer than the other decentralized strategies. Immunization based on JN is more efficient than other strategies, consuming around 12–40% less immunization resources to raise the epidemic threshold to 𝜏 ~ 0:1. Susceptible-Infected-Recovered (SIR) simulations on networks with 30% influential nodes removed using JN delays the arrival time of infection peak significantly and reduce the total infection scale to 15%.

scholarly journals Hunting for vital nodes in complex networks using local information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhihao Dong ◽  
Yuanzhu Chen ◽  
Terrence S. Tricco ◽  
Cheng Li ◽  
Ting Hu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Structure And Function ◽  
Local Information ◽  
Epidemic Threshold ◽  
Global Knowledge ◽  
The Real ◽  
And Function ◽  
Time Critical ◽  
Taking Action

AbstractComplex networks in the real world are often with heterogeneous degree distributions. The structure and function of nodes can vary significantly, with vital nodes playing a crucial role in information spread and other spreading phenomena. Identifying and taking action on vital nodes enables change to the network’s structure and function more efficiently. Previous work either redefines metrics used to measure the nodes’ importance or focuses on developing algorithms to efficiently find vital nodes. These approaches typically rely on global knowledge of the network and assume that the structure of the network does not change over time, both of which are difficult to achieve in the real world. In this paper, we propose a localized strategy that can find vital nodes without global knowledge of the network. Our joint nomination (JN) strategy selects a random set of nodes along with a set of nodes connected to those nodes, and together they nominate the vital node set. Experiments are conducted on 12 network datasets that include synthetic and real-world networks, and undirected and directed networks. Results show that average degree of the identified node set is about 3–8 times higher than that of the full node set, and higher-degree nodes take larger proportions in the degree distribution of the identified vital node set. Removal of vital nodes increases the average shortest path length by 20–70% over the original network, or about 8–15% longer than the other decentralized strategies. Immunization based on JN is more efficient than other strategies, consuming around 12–40% less immunization resources to raise the epidemic threshold to $$\tau \sim 0.1$$ τ ∼ 0.1 . Susceptible-infected-recovered simulations on networks with 30% vital nodes removed using JN delays the arrival time of infection peak significantly and reduce the total infection scale to 15%. The proposed strategy can effectively identify vital nodes using only local information and is feasible to implement in the real world to cope with time-critical scenarios such as the sudden outbreak of COVID-19.

Distributed learning automata-based algorithm for community detection in complex networks

2016 ◽  
Vol 30 (08) ◽  
pp. 1650042 ◽  
Author(s):  
Mohammad Mehdi Daliri Khomami ◽  
Alireza Rezvanian ◽  
Mohammad Reza Meybodi
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Learning Automata ◽  
Distributed Learning ◽  
Structure And Function ◽  
Label Propagation ◽  
Information Networks ◽  
Detection Algorithms ◽  
Distributed Learning Automata ◽  
And Function

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

scholarly journals The relationship between structure and function in locally observed complex networks

2013 ◽  
Vol 15 (1) ◽  
pp. 013048 ◽  
Author(s):  
Cesar H Comin ◽  
Matheus P Viana ◽  
Luciano da F Costa
Keyword(s):  
Complex Networks ◽  
Structure And Function ◽  
And Function ◽  
The Relationship

Structure and function of neural networks in the retina

1988 ◽  
Vol 46 ◽  
pp. 26-27
Author(s):  
Peter Sterling
Keyword(s):  
Ganglion Cells ◽  
Three Dimensional ◽  
Structure And Function ◽  
Synaptic Connections ◽  
Visual Axis ◽  
One Dimensional ◽  
Cat Retina ◽  
Ultrathin Sections ◽  
And Function ◽  
Insight Into

The synaptic connections in cat retina that link photoreceptors to ganglion cells have been analyzed quantitatively. Our approach has been to prepare serial, ultrathin sections and photograph en montage at low magnification (˜2000X) in the electron microscope. Six series, 100-300 sections long, have been prepared over the last decade. They derive from different cats but always from the same region of retina, about one degree from the center of the visual axis. The material has been analyzed by reconstructing adjacent neurons in each array and then identifying systematically the synaptic connections between arrays. Most reconstructions were done manually by tracing the outlines of processes in successive sections onto acetate sheets aligned on a cartoonist's jig. The tracings were then digitized, stacked by computer, and printed with the hidden lines removed. The results have provided rather than the usual one-dimensional account of pathways, a three-dimensional account of circuits. From this has emerged insight into the functional architecture.

Osseointegration interface of calcified bone and endosseous implants

1992 ◽  
Vol 50 (2) ◽  
pp. 1096-1097
Author(s):  
K.E. Krizan ◽  
J.E. Laffoon ◽  
M.J. Buckley
Keyword(s):  
Structure And Function ◽  
Titanium Implants ◽  
En Bloc ◽  
Endosseous Implants ◽  
Ultrastructural Studies ◽  
The Past ◽  
Cacodylate Buffer ◽  
One Year ◽  
And Function

With increase use of tissue-integrated prostheses in recent years it is a goal to understand what is happening at the interface between haversion bone and bulk metal. This study uses electron microscopy (EM) techniques to establish parameters for osseointegration (structure and function between bone and nonload-carrying implants) in an animal model. In the past the interface has been evaluated extensively with light microscopy methods. Today researchers are using the EM for ultrastructural studies of the bone tissue and implant responses to an in vivo environment. Under general anesthesia nine adult mongrel dogs received three Brånemark (Nobelpharma) 3.75 × 7 mm titanium implants surgical placed in their left zygomatic arch. After a one year healing period the animals were injected with a routine bone marker (oxytetracycline), euthanized and perfused via aortic cannulation with 3% glutaraldehyde in 0.1M cacodylate buffer pH 7.2. Implants were retrieved en bloc, harvest radiographs made (Fig. 1), and routinely embedded in plastic. Tissue and implants were cut into 300 micron thick wafers, longitudinally to the implant with an Isomet saw and diamond wafering blade [Beuhler] until the center of the implant was reached.

Use of human autoantibodies and immunoelectron microscopy to study structure and function of the nucleolus and nuclear bodies

1992 ◽  
Vol 50 (1) ◽  
pp. 506-507
Author(s):  
Robert L. Ochs
Keyword(s):  
Electron Microscopy ◽  
Structure And Function ◽  
Nuclear Bodies ◽  
Nuclear Interior ◽  
Coiled Bodies ◽  
Interchromatin Granules ◽  
And Function ◽  
Conventional Electron Microscopy ◽  
Perichromatin Granules ◽  
Perichromatin Fibrils

By conventional electron microscopy, the formed elements of the nuclear interior include the nucleolus, chromatin, interchromatin granules, perichromatin granules, perichromatin fibrils, and various types of nuclear bodies (Figs. 1a-c). Of these structures, all have been reasonably well characterized structurally and functionally except for nuclear bodies. The most common types of nuclear bodies are simple nuclear bodies and coiled bodies (Figs. 1a,c). Since nuclear bodies are small in size (0.2-1.0 μm in diameter) and infrequent in number, they are often overlooked or simply not observed in any random thin section. The rat liver hepatocyte in Fig. 1b is a case in point. Historically, nuclear bodies are more prominent in hyperactive cells, they often occur in proximity to nucleoli (Fig. 1c), and sometimes they are observed to “bud off” from the nucleolar surface.

Depletion and Reconstitution of Active E. Coli Ribosomes

1975 ◽  
Vol 33 ◽  
pp. 640-641
Author(s):  
M. Boublik ◽  
W. Hellmann ◽  
F. Jenkins
Keyword(s):  
Protein Biosynthesis ◽  
Large Subunit ◽  
High Resolution Electron Microscopy ◽  
Small Subunit ◽  
Structure And Function ◽  
Biologically Active ◽  
Biological Macromolecules ◽  
E Coli ◽  
Resolution Electron ◽  
And Function

Correlations between structure and function of biological macromolecules have been studied intensively for many years, mostly by indirect methods. High resolution electron microscopy is a unique tool which can provide such information directly by comparing the conformation of biopolymers in their biologically active and inactive state. We have correlated the structure and function of ribosomes, ribonucleoprotein particles which are the site of protein biosynthesis. 70S E. coli ribosomes, used in this experiment, are composed of two subunits - large (50S) and small (30S). The large subunit consists of 34 proteins and two different ribonucleic acid molecules. The small subunit contains 21 proteins and one RNA molecule. All proteins (with the exception of L7 and L12) are present in one copy per ribosome.This study deals with the changes in the fine structure of E. coli ribosomes depleted of proteins L7 and L12. These proteins are unique in many aspects.

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