Self-Similarity in Complex Networks
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Self-Similarity in Complex Networks. Building on a paper by O\'Shanker with the ultimate idea of applying fractal dimension to characterize the anatomy of a website.
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![Self-Similarity in Complex *) Networks ( social-biochemical-power/computer ) NF 10/10/2010 eVolution Strategy Team 1/11 *) complex vs. discrete regular lattice vs. random networks **) D. Watts; S. Strogatz; S. Miligram ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],small-worldness & Clustering (6-degree of Separation) **) Growth by preferential attachment Object in space vs. process in time (vibration in proteins)](https://image.slidesharecdn.com/cnfracdim-13135516457137-phpapp01-110816225153-phpapp01/85/Self-Similarity-in-Complex-Networks-1-320.jpg)
![[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Power law fulfills scaling V(r) = kr d V(ar) = ka d r d = g V(r) With g = ka d NF 10/10/2010 eVolution Strategy Team 2/11 “ small-worldness” 1-dim reg. Network: Measure via average path length: 60 nodes 15 Resilience & Vulnerability (not so easy) Notions & Plan 1000 nodes -> avg. 250 5% random rewired -> approx. 20 First 5 cause 50% cut in average path length for any N - If I had to make a brain – fast info travel + low cost maintanance, reliable long-distance connections….. contrast to: everything connects to everything else.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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wormhole by using some other algorithms. Our aim in
this paper is to deduce the traffic threshold level by
derivational approach for identifying wormholes in a very single phase in relay network having dissi
milar
characteristics.
Digital Wave Simulation of Lossy Lines for Multi-Gigabit Applications
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IRJET- Survey on Adaptive Routing Algorithms
This document discusses several adaptive routing algorithms for wireless networks and optical networks. It provides 3 key points:
1) Adaptive routing algorithms dynamically change their routing decisions based on changes in network topology and traffic levels. They aim to optimize parameters like distance, number of hops, and estimated transmission time.
2) Several adaptive routing algorithms are described, including centralized and distributed algorithms, algorithms using the A-star and Dijkstra's algorithms, and fault-aware algorithms to route around failed nodes or links.
3) Improved dynamic routing algorithms for elastic optical networks are proposed that select paths based on the number of links or weights accounting for link usage and available spectrum. The algorithms aim to efficiently route requests while
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Panel101R princeton.pdf
This document provides an introduction to panel data analysis using R. It discusses exploring panel data graphs, fixed effects models using least squares dummy variables to account for heterogeneity across groups, and random effects models. Fixed effects models estimate the within-entity relationship between the predictor and outcome variables, while random effects models estimate both the within-entity and between-entity relationships. The document uses examples with simulated panel data to demonstrate how to fit and interpret fixed and random effects models in R.
Performance evaluation of energy
Wireless sensor network are emerging in various fields like environmental monitoring, mining, surveillance
system, medical monitoring. LEACH protocol is one of the predominantly used clustering routing protocols
in wireless sensor networks. In Leach each node has equal chance to become a cluster head which make
the energy dissipated of each node be moderately balanced. We have pioneered an improved algorithm
named as Novel Leach based on Leach protocol. The proposed algorithm shows the significant
improvement in network lifetime .Comparison of proposed algorithm is done with basic leach in terms of
network life time, cluster head selection, energy consumption, and data transmission to base station. The
simulation results shows that proposed algorithm can reduce network energy consumption and prolong
network life commendably. Simulation of our protocol is done with Matlab.
A SIMULATION-BASED PERFORMANCE COMPARISON OF MANETS CDS CREATION ALGORITHMS U...
This document compares the performance of Wu and Li's Connected Dominating Set (CDS) creation algorithm and its location-information-based version under different Medium Access Control (MAC) protocols and network sizes. Simulations were conducted using an ideal MAC protocol and the IEEE 802.11 MAC protocol. The results show that using the 802.11 MAC produces smaller CDS sizes, lower signaling overhead, but longer establishment times compared to the ideal MAC. This is because packet collisions in the 802.11 MAC deprive nodes of accurate neighborhood information, leading to suboptimal CDS construction and longer delays.
11.design and implementation of distributed space frequency to achieve cooper...
This document summarizes a research paper that proposes a distributed space-frequency coding scheme to achieve cooperative diversity in wireless relay networks. The key points are:
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2) It considers diversity from source coding, channel coding, and user cooperation. The proposed space-time coded communication scheme is shown to be bandwidth and power efficient.
3) Performance is analyzed over fading channels using a maximum likelihood decision metric with channel estimation errors. Performance in slowly changing Rayleigh channels is also presented.
IRJET- Synchronization Scheme of MIMO-OFDM using Monte Carlo Method
This document proposes a synchronization scheme for MIMO-OFDM using the Monte Carlo method. It involves using an iterative turbo receiver with two levels - a soft-input soft-output SSMC detector followed by a soft channel decoder. The detector and decoder exchange information iteratively to reduce the bit error rate. Simulation results show that the proposed Monte Carlo simulation method decreases error rate as the number of transmitters and receivers increases. Bit error rate and mean squared error rate are compared for different signal-to-noise ratios to demonstrate the performance of the system.
C:\Documents And Settings\User\桌面\Sciece Talk投影片\Science Talk 100111 陸行
The document discusses computing waiting times for queueing systems using mathematical models. It introduces concepts such as average waiting time, variance of waiting time, probability distribution functions of waiting time, and models like M/M/1 queues. It provides examples of applying these concepts and models to analyze single line and multiple line queues, and compares the performance of single vs multiple server systems.
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Using Genetic Algorithm for Shortest Path Selection with Real Time Traffic Flow
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DIGITAL WAVE SIMULATION OF LOSSY LINES FOR MULTI-GIGABIT APPLICATION
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International Journal of Engineering Inventions (IJEI)
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C:\Documents And Settings\User\桌面\Sciece Talk投影片\Science Talk 100111 陸行
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Self-Similarity in Complex Networks
- 4. NF 10/10/2010 eVolution Strategy Team 4/11 //for a range of cross-cut probability , i.e. move from 1-dim to 2-dim for (int iProb = 0; iProb <= iProbMax; iProb += 1) { m_ShortCutProbability = iProb; //Make new network! MakeNetwork(); Application.DoEvents(); //for a range of radi i , e.g. 10 - 60 for (int iRadius = 10; iRadius <= iRadiusMax; iRadius += 10) { iVol = 0; lbl_Radius.Text = iRadius.ToString(); //for various center-nodes (5% of total)– next time better mix (or use ALL) – reduce fluctuation for (int iNumOfCenterNode = 0; iNumOfCenterNode < iNOfCNodes; iNumOfCenterNode++) { m_NodeAtCenter = m_NW.CNetworkNodes[rnd.Next(0, m_NumOfNodes - 1)]; iVol += DetermineVolume (m_NodeAtCenter, iRadius); Application.DoEvents(); } //average over the number of center-nodes iVol = iVol / iNOfCNodes; lbl_Volume.Text = iVol.ToString(); m_VolGraph.AddPoint(iRadius, iVol, iSeries); //for Prob = 0 we have Vol = 2*r + 1 m_Utils.WriteToTextfile("r = " + iRadius.ToString() + " Vol = " + iVol.ToString()); m_Utils.XRaw.Add(Math.Log10(iRadius)); m_Utils.YRaw.Add(Math.Log10(iVol)); Application.DoEvents(); } //Calculate fractal Dimension – see straight line on log-log plot – hopefully ! m_Utils.LinearRegression(); - Thereby get a FD for each cross-cut probability JUST DO IT ! d p V r
- 5. NF 10/10/2010 eVolution Strategy Team 5/11
- 6. 6/11
- 7. NF 10/10/2010 eVolution Strategy Team 7/11
- 8. Check: Probability 0. Volume = 2 * r + 1 r = 10 Vol = 21 r = 20 Vol = 41 r = 30 Vol = 61 r = 40 Vol = 81 r = 50 Vol = 101 0.999985555312809 = correlation 0.975798184581066 slope = fractal dimension d 0.345057434182423 intercept => k = 10^ intercept constant typical for th First clues/results: + CPN scale free with respect to volume ala O’Shanker (use on webpage network) + Fluctuations typical for choice of numerical parameters (edge effects) + d -business-value-attribution ain’t straight forward…perhaps empirical (projection/convolution) + Requires experimentation, i.e. time & research NF 10/10/2010 eVolution Strategy Team 8/11 V(R) = k *r d
- 9. NF 10/10/2010 eVolution Strategy Team 9/11
- 10. NF 10/10/2010 eVolution Strategy Team 10/11
- 11. 11/11 NF 10/10/2010 eVolution Strategy Team