{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#
Structural Analysis and Visualization of Networks
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Course Project #1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###
Student: *{Your Name}*
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####
General Information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Due Date:** 17.05.2015 23:59
\n", "**Late submission policy:** -0.2 points per day
\n", "\n", "\n", "Please send your reports to and with message subject of the following structure:
**[HSE Networks 2015] *{LastName}* *{First Name}* Project*{Number}***\n", "\n", "Support your computations with figures and comments.
\n", "If you are using IPython Notebook you may use this file as a starting point of your report.
\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a dataset to analyse you can choose one option in the following list:\n", "1. Real Dataset (can be found [here](http://snap.stanford.edu/) or [here](http://konect.uni-koblenz.de/networks/))\n", "2. Generated Dataset. Use more complex structure rather than just a simple ER model. For instance, you may consider multilevel network, where on the lower level you have several Watts-Strogatz graphs and on the upper level these graphs are respesented as randomly connected nodes.\n", "3. Your data mined from Social Networks, Twitter, LiveJournal e.t.c.\n", "\n", "**The order of your dataset should be no less than $10^4$ nodes**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider one of the following models:\n", "1. SIR-based (or another with more than 3 letters) epidemic model\n", "2. Independent Cascade Model\n", "3. Linear Threshold Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tasks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Network Descriptive Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Provide information on your netowork: Source, Descriptive Statistics, Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Main Task for model (1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You are in charge of leading the vaccination campaign against some outbroken nonlethal disease. You have options to vactinate or provide medical treatment to infected ones. However, everything has its costs:\n", "* Vaccination of a node costs $500 \\$$ and make it immune to the disease all life-long. Unfortunately, you can help this way only to no more than $10\\%$ of your population\n", "* Medical Treatment costs $120\\$$ per day of illness period, which in turn may take from $3$ to $7$ days\n", "\n", "Your task is to implement the simulation model, propose some vaccination strategies and compare them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Main Task for models (2-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You are running the marketing campaign for brand new pocket device. Initially you can sign contracts with a few people to advertize your gadget among their neigbours. The more \"famous\" person you are picking the greater price appears in the contract.\n", "* Contract cost can be calculated as $300 \\$ \\times \\text{NN}(i)$, where $\\text{NN}(i)$ is size of the neigbourhood of the person $i$.\n", "* You earn $250\\$$ per each affected person\n", "\n", "Your task is to maximize your influence and maximize profit of your campaign" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }