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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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