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applied-social-network-analysis-in-python---university-of-michigan---coursera

This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

ba-scalefreenetwork

barabasi albert algorithm to generate a scale free network.

complex_networks

Simulate complex networks with different models(small world, scale-free, random), and compare the robustness (under random failures) of them.

complexnetworksmeasurements

Algorithms to measure some networks characteristics like robustness and fractality

e-net

The code of paper "Robust Network Enhancement from Flawed Networks"

ec-rsf

A Heuristic Algorithm for Enhancing the Robustness of Scale-free Networks based on Edge Classification

evolving_network

Python code for evolving complex networks to maximize their robustness

growing-networks

A lot of connections can be described using network theory: hyperlinks between websites, citations between papers, relationships between people etc. This project aims to study how properties of a network, such as degree distribution (number of connections per point), change as the network grows, tweaking the ways with which the network grows. A scale-free behaviour (similar to as seen in Complexity Science) can be found in networks as well.

locus-based-genetic-algorithm

Apply locus-based genetic algorithm (LGA) to identify community structure of networks.

mesh_optimize

Research new algorithm for IOT/Mesh network to accelerate convergence and enhance robust.

moea-net-hl

The relevant codes of our work "Enhancing Robustness and Transmission Performance of Heterogeneous Complex Networks via Multi-Objective Optimization".

network-spreading

Simulate the network-spreading on three theoretical networks including a random, a scale-free network and a small-world network.

prediction-of-arrival-of-nodes-in-a-scale-free-network

We present in this article, a novel strategy to partially predict the order of node arrivals in such an evolved network. We show that our proposed method outperforms other centrality measure based approaches.

pytorch-tutorial

Build your neural network easy and fast

railway-network-system

A network optimization application where the user interface represents the railway zonal map of India with zonal headquarters(junctions) as nodes and and connection between the nodes represent the broad gauge line . It is a robust application where the user can search the routes between two railway junctions and the application displays the route on the user interface.It is dynamic application whereby for example if due to accident or natural calamity certain routes are marked as blocked/not working then the application itself will compute an alternative path omitting the blocked routes.

robustness

Software for evaluating the robustness of complex networks.

sampling-robustness

Measuring Sampling Robustness of Complex Networks

scalefree_and_random_graphs

As a part of Networks Data Analyzing course, this project creates a 2 connected sub-graph where each of them builded according to Scale Free and Random algorithms

social_network_analysis

We will discuss network analysis through tutorials using the NetworkX library. Starting with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

test

worstcasedominatingset

Approximating optimization problems using EAs on scale-free networks