Analyzing the Small World Phenomenon Using a Hybrid Model with Local Network Flow (Extended Abstract)

AbstractAgro-ecosystems are human-managed natural systems, and therefore are subject to generalized ecological rules. A deeper understanding of the factors impacting on the biotic component of ecosystem stability is needed for promoting the sustainability and productivity of global agriculture. Here we propose a method to determine ecological emergent properties through the inference of network properties in local microbial communities, and to use them as biomarkers of the anthropogenic impact of different farming practices on vineyard soil ecosystem functioning. In a dataset of 350 vineyard soil samples from USA and Spain we observed that fungal communities ranged from random to small-world network arrangements with differential levels of niche specialization. Some of the network properties studied were strongly correlated, defining patterns of ecological emergent properties that are influenced by the intensification level of the crop management. Low-intervention practices (from organic to biodynamic approaches) promoted densely clustered networks, describing an equilibrium state based on mixed (generalist-collaborative) communities. Contrary, in conventionally managed vineyards, we observed highly modular (niche-specialized) low clustered communities, supported by a higher degree of selection (more co-exclusion proportion). We also found that, although geographic factors can explain the different fungal community arrangements in both countries, the relationship between network properties in local fungal communities better capture the impact of farming practices regardless of the location. Thus, we hypothesize that local network properties can be globally used to evaluate the effect of ecosystem disturbances in crops, but also in when evaluating the effect of clinical interventions or to compare microbiomes of healthy vs. disturbed conditions.

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Runtime Service Discovery for Grid Applications

Grid Technology for Maximizing Collaborative Decision Management and Support ◽

10.4018/978-1-60566-364-7.ch011 ◽

2009 ◽

pp. 212-234 ◽

Cited By ~ 2

Author(s):

James Dooley ◽

Andrea Zisman ◽

George Spanoudakis

Keyword(s):

Social Networks ◽

Service Discovery ◽

Large Scale ◽

Virtual Organisations ◽

Small World ◽

Resource Discovery ◽

Local Network ◽

Network Node ◽

Grid Resource ◽

New Generation

A Virtual Organisation in large-scale distributed systems is a set of individuals and/or institutions with some common purposes or interests that need to share their resources to further their objectives, which is similar to a human community in social networks that consists of people have common interests or goals. Due to the similarity between social networks and Grids, the concepts in social science (e.g. small world phenomenon) can be adopted for the design of new generation Grid systems. This chapter presents a Small World Architecture for Effective Virtual Organisations (SWEVO) for Grid resource discovery in Virtual Organisations, which enables Virtual Organisations working in a more collaborative manner to support decision makers. In SWEVO, Virtual Organisations are connected by a small number of interorganisational links. Not every local network node needs to be connected to remote Virtual Organisations, but every network node can efficiently find connections to specific Virtual Organisations.

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Adaptive Rewiring on Coupled Logistic Maps with Heterogeneous Parameters

10.31237/osf.io/jg2zc ◽

2020 ◽

Author(s):

MohammadHossein Manuel Haqiqatkhah

Keyword(s):

Path Length ◽

Initial Conditions ◽

Brain Plasticity ◽

Small World ◽

Average Path Length ◽

Clustering Coefficient ◽

Local Network ◽

Edge Density ◽

Multivariate Distributions ◽

Functional Components

Adaptive rewiring is the driving force of brain plasticity to form modular, small-world connectivity structure. Highly simplified models for adaptive rewiring represent the dynamic activity of neural masses by coupled logistic maps. Such models have thus far used uniform parametrizations, preventing any cognitive functionality. In order to enable cognitive functions, adaptive rewiring has to be robust to non-uniformity of parameters. Moreover, it should enable function-specific structures to emerge from such parameterization. Coupled logistic maps are characterized by two parameters, namely turbulence (denoted by \alpha), controlling the range of node activation and coupling strength (denoted by \mathcal{E}) between nodes. We study five parameterization conditions of an adaptively rewiring coupled map networks. A baseline (BL) condition with uniform values for \alpha and \mathcal{E} is compared with four conditions in which either \alpha or \mathcal{E} of a subset (viz., minority subgraph) deviates from the remaining units (majority subgraph). These conditions are called: under-turbulent (UT) or over-turbulent (OT), according to \alpha; and under-coupled (UC) or over-coupled (OC), according to \mathcal{E}. For each condition, 10 model instantiations are run for 20 million discrete updates and undergo one million adaptive rewiring steps. To describe the evolution of model network structure, the clustering coefficient, average path length, small-worldness, modularity, degree assortativity, and edge density are calculated, both for the whole network and its minority and majority subgraphs separately. The rich club coefficient is calculated for the final state of the network. Different conditions are compared by representing networks as multivariate distributions of local network statistics. Aggregate scores of within- and between-condition contrast are defined to measure dissimilarity amongst conditions. The degree to which conditions diverge is evaluated statistically. The results show adaptive rewiring to improve all measures substantially (except for average path length), such that highly modular, small-world network structures evolve from random initial conditions, while the structures evolving in different conditions show considerable differentiation.This study offers computational support for robustness of adaptive rewiring algorithm under symmetry-breaking conditions regarding the dynamic evolution of properties characteristic to brain networks. Furthermore, function-specific structures and behaviors emerge from such deviations, implying that functional and structural differentiation can be used to identify functional components in a network, upholding the use of structural and functional connectivity measures in neuroimaging.

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Modeling and protecting global ecological networks

International Journal of Modern Physics C ◽

10.1142/s0129183121500467 ◽

2021 ◽

pp. 2150046

Author(s):

Xinmiao Sun ◽

Ruiqi Li

Keyword(s):

Trophic Level ◽

Human Activities ◽

Degree Distribution ◽

Small World ◽

Volterra Model ◽

Local Network ◽

Ecological Network ◽

Rapid Urbanization ◽

Local Networks ◽

Quantitative Changes

With the rapid urbanization worldwide and ever-increasing impacts of human activities since at least 200 years ago, we are now facing a harsh situation of our biosphere. Building a global-level network model on ecological systems is of great importance, which would be able to provide us predictive and quantitative responses to human activities, leading to viable suggestions to policymakers. In this paper, we propose a multi-layer model for the global ecological network, where a number of local networks are connected via long-range interactions associated with migrant species, which can be induced by human activities or natural migration of wildlife, and each local network is generated by a trophic-level-based stochastic model. Predator–prey dynamics is described by a networked Lotka–Volterra model that accounts for the self-suppression effects on basal species, and the negative feedback loops. Impacts of human activities are modeled by investigating the quantitative changes of biodiversity under certain protecting strategies. We reveal that the global ecological network is organized in a clustered small-world manner, with in-degree distribution more heterogeneous than out-degree distribution. Protecting endangered species, popular preys and predicted-to-be-extinct species is more effective than randomly selected species or influential predators. Protecting after entering the fast extinction stage is more effective than at the beginning for some high trophic level species.

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Efficient Rule Set Generation using Rough Set Theory for Classification of High Dimensional Data

International Journal of Smart Sensor and Adhoc Network. ◽

10.47893/ijssan.2011.1036 ◽

2011 ◽

pp. 129-136

Author(s):

Prasanta Gogoi ◽

Ranjan Das ◽

B Borah ◽

D K. Bhattacharyya

Keyword(s):

Set Theory ◽

Rough Set ◽

Network Flow ◽

Rough Set Theory ◽

Real Life ◽

Local Network ◽

Upper Approximation ◽

Inconsistent Data ◽

Network Intrusion ◽

Rule Set

In this paper, a rough set theory (RST) based approach is proposed to mine concise rules from inconsistent data. The approach deals with inconsistent data. At first, it computes the lower and upper approximation for each concept, then adopts a learning from an algorithm to build concise classification rules for each concept satisfying the given classification accuracy. Lower and upper approximation estimation is designed for the implementation, which substantially reduce the computational complexity of the algorithm. UCI ML Repository datasets are used to test and validate the proposed approach. We have also used our approach on network intrusion dataset captured using our local network from network flow. The results show that our approach produces effective and minimal rules and provide satisfactory accuracy over several real life datasets.

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