Incremental Networks Research Articles

InOvIn: A fuzzy-rough approach for detecting overlapping communities with intrinsic structures in evolving networks

Real-world networks, such as biological, biomedical and social networks, often contain overlapping and intrinsic communities. More significantly, such networks are growing or evolving over time, which leads to a continuous alteration of community structures. Detecting overlapping community together with intrinsic structures in evolving scenarios is one of the challenging tasks. Prior researches are limited in handling all the events together while designing a community detector.We propose an integrated solution, InOvIn (Intrinsic Overlapping Community Detection in Incremental Networks), for detecting overlapping, non-overlapping and intrinsic communities in evolving networks. Herein, we have explored a rough-fuzzy clustering approach for overlapping community detection. Fuzzy membership helps in soft decision making for deciding membership of a node towards a target community. While rough boundary of the communities decides the shared membership of a node in multiple communities. The node degree density variation measure is used to discover the existence of intrinsic community within a community.We assess the performance of InOvIn in light of twelve (12) popular real-world social networks. It may be noted that available real-world networks are lacking in labeled overlapping and intrinsic communities. Hence, we synthetically generate six (06) networks with both overlapping and intrinsic communities. We demonstrate the superiority of InOvIn over contemporary community detection methods using ten (10) different statistical assessment parameters. Interestingly, for the first time, our method detects intrinsic communities in PolBooks and Word Adjacencies networks.

Theoretical Analysis of the Underwater Incremental Adaptive Network Performance Based on the VLC Technology

In this paper, the underwater implementation of the incremental adaptive networks is proposed based on the visible light communication technology. The underwater distance between transmitter and receiver nodes and the salinity and temperature levels of the considered water determines the stochastical properties of the underwater link that is modeled with the Log-normal distribution. The incremental network performance can be expressed with the excess mean square error and mean square deviation values and we used them in this paper for our theoretical analysis. Our findings showed that the distances between the nodes must not be more than 10 m or the incremental network will diverge from its estimation goal. The network performance is analyzed through multiple link distances and the results are presented with several simulations. The simulation results are devised in order to elaborate the effects of the underwater turbulent links on the performances of estimating adaptive network. Also, the impacts of different salinity and temperature levels are analyzed theoretically and the results are compared with the simulation results.

Performance Analysis of Wireless Adaptive Incremental Networks Under Strong FSO Link Turbulence Conditions

The aim of this paper is to show the problems of implementing the wireless adaptive networks with the free space optical (FSO) technology. Implementing adaptive networks with the wireless optical communication technology has several benefits and also some hindering problems. The thermal optical noise modeled with Gaussian distribution and link turbulence is two of the major problems of this implementation. In this paper, the theoretical analysis of the FSO link effects that are modeled with K-distribution and Negative exponential distributions are considered on the estimation performance of the adaptive incremental networks. These distributions arise when the FSO link is contaminated with strong optical turbulence. Experiments are designed to cover these conditions and the analysis is based on the steady state mean square deviation (MSD) and excess mean square error (EMSE) values for the incremental LMS (ILMS) algorithm and these are the metrics that show how well the adaptive network performs. Simulation results are presented for different parameters of $K$ -distribution and negative exponential distribution and the results show perfect match with the theoretical outcomes. Based on these results, we show that implementing the incremental adaptive networks in the strong turbulence conditions is not feasible and we must think of some countermeasures for these cases.

The node distance effects on the performance of the underwater VLC incremental adaptive networks

ABSTRACTIn this paper, an underwater implementation of incremental networks based on visible light communication technology is described and analysed. The underwater distance between transmitter and receiver nodes and the salinity and temperature levels of the water yield stochastic properties of the link that can be modelled with the Log-normal distribution. The incremental network performance can be expressed using excess mean square error and Mean square deviation metrics. Our findings showed that the distance between nodes must not exceed 10 metres; if it does, the network performance will diverge from its estimation goal. The performance is analysed through multiple link distances and results are presented for simulations. The impacts of various salinity and temperature levels are analysed simultaneously.

A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State.

Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods.

Incorporating Observation Quality Information into the Incremental LMS Adaptive Networks

In this paper we investigate the effect of observation quality information (OQI) on the performance of a special class of adaptive networks known as distributed incremental least-mean square (DILMS) algorithm. To this aim we consider two different cases: (1) a homogeneous environment where all the nodes have the same observation noise variance (ONV) and (2) an inhomogeneous environment, where different nodes have different ONVs. In the first case we show that, for the same steady-state error, the DILMS algorithm has faster convergence rate in comparison with a non-cooperative scheme. In the second case, we first show that regardless of what ONVs are, the steady-state curves of mean-square deviation, excess mean-square error and mean square error (MSE) in each node are monotonically increasing functions of step-size parameter. Then, to use the OQI, we reformulate the parameter estimation as a constrained optimization problem with MSE criterion as the cost function and ONVs as the constraints. Using the Robbins-Monro method to solve the resultant problem, a new algorithm (which we call noise-constrained incremental LMS algorithm) is obtained which has faster convergence rate than the existing incremental LMS algorithm. Simulation results are also provided to clarify the performance of proposed algorithm.

Low computational complexity family of affine projection algorithms over adaptive distributed incremental networks

This paper presents the problem of distributed estimation in an incremental network based on the family of affine projection (AP) adaptive algorithms. The distributed selective partial update normalized least mean squares (dSPU-NLMS), the distributed SPU-AP algorithm (dSPU-APA), the distributed selective regressor APA (dSR-APA), the distributed dynamic selection of APA (dDS-APA), dSPU-SR-APA and dSPU-DS-APA are introduced in a unified way. These algorithms have low computational complexity feature and close convergence speed to ordinary distributed adaptive algorithms. In dSPU-NLMS and dSPU-APA, the weight coefficients are partially updated at each node during the adaptation. In dSR-APA, the optimum number of input regressors is selected during the weight coefficients update. The dynamic selection of input regressors is used in dDS-APA. dSPU-SR-APA and dSPU-DS-APA combine SPU with SR and DS approaches. In these algorithms, the weight coefficients are partially updated and the input regressors are optimally/dynamically selected at every iteration for each node. In addition, a unified approach for mean-square performance analysis of each individual node is presented. This approach can be used to establish a performance analysis of classical distributed adaptive algorithms as well. The theoretical expressions for stability bounds, transient, and steady-state performance analysis of various distributed APAs are introduced. The validity of the theoretical results and the good performance of dAPAs are demonstrated by several computer simulations.

ILP Model and Relaxation-Based Decomposition Approach for Incremental Topology Optimization inp-Cycle Networks

p-cycle networks have attracted a considerable interest in the network survivability literature in recent years. However, most of the existing work assumes a known network topology upon which to applyp-cycle restoration. In the present work, we develop an incremental topology optimization ILP forp-cycle network design, where a known topology can be amended with new fibre links selected from a set of eligible spans. The ILP proves to be relatively easy to solve for small test case instances but becomes computationally intensive on larger networks. We then follow with a relaxation-based decomposition approach to overcome this challenge. The decomposition approach significantly reduces computational complexity of the problem, allowing the ILP to be solved in reasonable time with no statistically significant impact on solution optimality.

Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP

This article compares the performance of some recently developed incremental neural networks with the wellknown multilayer perceptron (MLP) on real-world data. The incremental networks are fuzzy ARTMAP (FAM), growing neural gas (GNG) and growing cell structures (GCS). The real-world datasets consist of four different datasets posing different challenges to the networks in terms of complexity of decision boundaries, overlapping between classes, and size of the datasets. The performance of the networks on the datasets is reported with respect to measure classification error, number of training epochs, and sensitivity toward variation of parameters. Statistical evaluations are applied to examine the significance of the results. The overall performance ranks in the following descending order: GNG, GCS, MLP, FAM.

Localized BF-Type Networks for Identification and Adaptive Control of Discrete-Time Nonlinear Systems

Abstract Local Model Networks and Neurofuzzy systems with localized basis functions are analysed under a common framework, as instances of localized basis functions-type networks. Their use for identification and adaptive control of discrete-time nonlinear systems is addressed, considering aspects as the use of robust adaptation laws, incremental networks, and others. Some questions are posed concerning these aspects.