00_03._networks_ha3.ipynb

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   "source": [
    "# <center>Network Science</center>"
   ]
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   "source": [
    "## <center>Home Assignment #3: Centralities and Assortativity</center>"
   ]
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   "metadata": {},
   "source": [
    "### <center>Student: *{Your Name}*</center>"
   ]
  },
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   "metadata": {},
   "source": [
    "#### <hr /> General Information"
   ]
  },
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   "metadata": {},
   "source": [
    "**Due Date:** 13.03.2016 23:59 <br \\>\n",
    "**Late submission policy:** -0.2 points per day <br \\>\n",
    "\n",
    "\n",
    "Please send your reports to <mailto:network.hse.2016@gmail.com> with message subject of the following structure:<br \\> **[HSE Networks 2015] *{LastName}* *{First Name}* HA*{Number}***\n",
    "\n",
    "Support your computations with figures and comments. <br \\>\n",
    "If you are using IPython Notebook you may use this file as a starting point of your report.<br \\>\n",
    "<br \\>\n",
    "<hr \\>"
   ]
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   "source": [
    "## Problems"
   ]
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   "source": [
    "### Task 1"
   ]
  },
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   "source": [
    "Compute degree centrality, Pagerank and  HubAuthorities scores for the [flickr](https://www.hse.ru/data/2016/02/24/1139618969/flickr.mat) network. \n",
    "\n",
    "Data contains sparse matrix A and list of user names.\n",
    "This is a “denser” part of the Flickr photo sharing site friendship graph from 2006. Edge direction corresponds to friendship requests (following). Some of the links are reciprocal,others not.  \n",
    "\n",
    "Provide top 50 names in each ranking, compare results\n",
    "\n",
    "You can load .mat files with the following commands:"
   ]
  },
  {
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   "execution_count": null,
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   "source": [
    "import scipy.io\n",
    "\n",
    "data = scipy.io.loadmat('filename.mat')"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <hr /> Task 2"
   ]
  },
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   "source": [
    "Here are the [Facebook friendship graphs](https://snap.stanford.edu/data/egonets-Facebook.html) from several US universities from 2005 (one year after fb launch).\n",
    "\n",
    "Data contains a A matrix (sparse) and a \"local_info\" variable, one row per node: \n",
    "a student/faculty status flag, gender, major, second major/minor (if applicable), dorm/house, year, and high school. \n",
    "Missing data is coded 0.\n",
    "\n",
    "Compute node degree assortativity (mixining by node degree) and assortativity coefficient (modularity) for gender, major, dormitory, year, high school for all universities and compare the results."
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