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Created March 25, 2015 01:56
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SocialNetworkProject.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Structures and Algorithm Project"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Title: Graph Theoretical Modeling of a Social Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Objectives: </br>\n",
"\n",
"<ol>\n",
"\n",
"<li> To be able to utilize a graph data structure to represent the people (nodes) \n",
"and friendships in a social network. The social graph should contain at least 50 nodes. </li>\n",
"\n",
"<li> To be able to implement either Adjacency-Matrix representation or Adjacency-List \n",
"representation of Graph ADT. </li>\n",
"\n",
"<li> To be able to implement simple Euler and Hamilton paths in the social network graph. </li>\n",
"\n",
"<li> To be able to implement Depth First Search (DFS) and Breadth-First Search (BFS) to \n",
"the social network graph. </li>\n",
"\n",
"<li> To be able to represent a social network graph as a Minimum Spanning Tree using either\n",
"Prim's, Kruskal's, or Boruvka's algorithm. </li>\n",
"\n",
"<li> To be able to apply shortest path algorithms, i.e. Dijkstra's algorithm to the social network. </li>\n",
"\n",
"<li> (OPTIONAL) To be able to apply Network flow algorithms, i.e. Mincost-Flow Reductions, to the social network. </li>\n",
"\n",
"</ol>\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Starter Files </br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The starter program has already been implemented and can be accessed here:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"100\"\n",
" src=\"https://docs.google.com/a/dlsu.edu.ph/file/d/0B6GYu_QtcFzQNm1Db3ptazByd28/preview\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0xb5435c2c>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame\n",
"IFrame('https://docs.google.com/a/dlsu.edu.ph/file/d/0B6GYu_QtcFzQNm1Db3ptazByd28/preview', width='100%', height=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: Please use your DLSU gmail account to access the file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this project, we are extending Stanford CS 106a's Assignment (FacePamphlet):"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"880\"\n",
" src=\"https://docs.google.com/a/dlsu.edu.ph/file/d/0B6GYu_QtcFzQdnBXQmdWdkZpRTg/preview\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0xb5435d2c>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame\n",
"IFrame('https://docs.google.com/a/dlsu.edu.ph/file/d/0B6GYu_QtcFzQdnBXQmdWdkZpRTg/preview', width='100%', height=888)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Note: Algorithm details will be discussed in the lectures."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deliverables </br>\n",
"\n",
"<ol>\n",
"\n",
"<li> Documentation Report (with 2 initial progress reports): 40 % </li> \n",
"\n",
"<li> Prototype: 30 % </li>\n",
"\n",
"<li> Presentation: 30 % </li>\n",
"\n",
"</ol>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deadlines:\n",
"\n",
"<ol>\n",
"\n",
"<li> First Progress Report: <font color='red'> April 7, 2015 (Tue) </font> </li> \n",
"\n",
"<li> Second Progress Report: <font color='red'> April 14, 2015 (Tue) </font> </li> \n",
"\n",
"<li> Final Documentation Report, Prototype, and Presentation: <font color='red'> April 20, 2015 (Mon) </font> </li>\n",
"\n",
"</ol>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-mkc"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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