{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "X7LtDBAM41bV" }, "source": [ "# Centralities: who's the most important?\n", "\n", "
\n", " \n", " \n", " Open this notebook in Google Colab\n", " \n", "
\n", "\n", "\n", "
\n", " \n", " \n", " Download this notebook (File -> Save As)\n", " \n", "
\n", "\n", "For this assignment we will be exploring several centralities to get an intuitive sense of what the various centrality metrics tell us about the nodes in the graph. \n", "\n", "## Degree centrality and Eigenvector centrality\n", "\n", "Before messing with data, let's think about the two foundational centralities: degree centrality and eigenvector centrality.\n", "\n", "Degree centrality is simply defined as the number of edges connected to a node. If you imagine a social network where each node (person) can influence others, then the degree centrality captures the idea of how _directly_ influential a person would be. This may be a good first approximation. If someone is connected to so many people (e.g., those with many followers on a social media platform), then it is likely that the person is influential.\n", "\n", "Still, this assumes that all connections (followers) are more or less equal. But in reality, some followers may be more influential than others. For instance, imagine a guru followed by hugely influential politicians and celebrities. Even if the guru may not be directly connected to many people, the guru's influence can be huge.\n", "\n", "This is where the eigenvector centrality comes in. The idea is an extension of the degree centrality. Instead of defining the centrality of a node as its degree, eigenvector centrality defines the centrality of a node as the sum of eigenvector centrality of the neighbors. In other words, an important node is not just a node with many neighbors, but the one with many _important_ neighbors. We can think about repeating the following process:\n", "\n", "$$ x^{t+1}_i = \\frac{1}{\\lambda} \\sum_{j=1}^{n} A_{ij} x^{t}_j $$\n", "\n", "where $A$ is the adjacency matrix of the graph, $x$ is the eigenvector centrality vector, and $\\lambda$ is a normalization constant. It turns out, as you have learned, mathematically this idea can be translated into finding the _eigenvector_ of the adjacency matrix that corresponds to the largest (and positive) eigenvalue. \n", "\n", "$$ \\mathbf{A} \\mathbf{x} = \\lambda \\mathbf{x} $$\n", "\n", "**Here is a question: consider an undirected $k$-regular graph with only one connected component. That means that everyone can be reached from everyone else and every node's degree is $k$. What would be the eigenvector centrality vector of this graph?**" ] }, { "cell_type": "markdown", "metadata": { "id": "XDhmrT1h-SNq" }, "source": [ "
\n",
    "# YOUR SOLUTION HERE\n",
    "
" ] }, { "cell_type": "markdown", "metadata": { "id": "mrrsca6Q-Oz6" }, "source": [ "## Find the most important dolphin!\n", "\n", "We will be using the [Dolphin social network](http://www-personal.umich.edu/~mejn/netdata/dolphins.zip). (You may need to copy the link address into a new tab/window to trigger the download.) Download the graph and load it as a networkx graph." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 37 }, "executionInfo": { "elapsed": 166, "status": "ok", "timestamp": 1644788782868, "user": { "displayName": "YY Ahn", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgrTZL7cTLeROflLUdJfPKVrCE-B1oLucXDhc1hHA=s64", "userId": "10659256486022322110" }, "user_tz": 300 }, "id": "NL3cxlc_41bX", "outputId": "817713a6-645b-46e9-8089-967ec926a47c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of nodes: 62\n", "Number of edges: 159\n", "Average degree: 2.564516129032258\n" ] } ], "source": [ "import networkx as nx\n", "dolphin_social_network = nx.read_gml('dolphins.gml')\n", "\n", "# number of nodes, edges, and average degree\n", "num_nodes = len(dolphin_social_network.nodes())\n", "num_edges = len(dolphin_social_network.edges())\n", "avg_degree = num_edges / num_nodes\n", "print('Number of nodes:', num_nodes)\n", "print('Number of edges:', num_edges)\n", "print('Average degree:', avg_degree)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 320 }, "executionInfo": { "elapsed": 284, "status": "ok", "timestamp": 1644788783799, "user": { "displayName": "YY Ahn", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgrTZL7cTLeROflLUdJfPKVrCE-B1oLucXDhc1hHA=s64", "userId": "10659256486022322110" }, "user_tz": 300 }, "id": "T0gsreCd5iNM", "outputId": "09338b9a-05df-4550-9c33-a1813b4ac0db" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(dolphin_social_network)" ] }, { "cell_type": "markdown", "metadata": { "id": "l8Gu-s-M41bX" }, "source": [ "## Centrality in Networkx\n", "Networkx has several functions available for calculating the centralities of the nodes in the graph. There are functions for [eigenvector](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.eigenvector_centrality.html?highlight=eigenvector#networkx.algorithms.centrality.eigenvector_centrality), [katz](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.katz_centrality.html#networkx.algorithms.centrality.katz_centrality), [closeness](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.closeness_centrality.html#networkx.algorithms.centrality.closeness_centrality), [betweenness](https://networkx.org/documentation/stable/reference/algorithms/centrality.html#shortest-path-betweenness), [degree](https://networkx.org/documentation/stable/reference/algorithms/centrality.html#degree), etc. For a full list you can visit the [documentation page](https://networkx.org/documentation/stable/reference/algorithms/centrality.html#module-networkx.algorithms.centrality). \n", "\n", "These functions take a graph as an argument and return a dictionary with nodes as keys and the centrality as values. This is convenient for us because we can set these as attributes for the nodes in the graph using the [`set_node_attributes`](https://networkx.org/documentation/stable/reference/generated/networkx.classes.function.set_node_attributes.html?highlight=set%20node%20attributes#networkx-classes-function-set-node-attributes) function. For example:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 406, "status": "ok", "timestamp": 1644789329802, "user": { "displayName": "YY Ahn", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgrTZL7cTLeROflLUdJfPKVrCE-B1oLucXDhc1hHA=s64", "userId": "10659256486022322110" }, "user_tz": 300 }, "id": "5mz08Rbd41bX", "outputId": "194aa7fa-a366-4578-da5c-75b737f34f4f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.04926750308526889\n" ] } ], "source": [ "import networkx as nx\n", "\n", "my_graph = nx.erdos_renyi_graph(500, 0.3)\n", "\n", "# Get the eigenvector centralities for all the nodes\n", "centralities = nx.eigenvector_centrality(my_graph)\n", "\n", "# Set the attributes of the nodes to include the centralities\n", "# The arguments are: \n", "# Where is a dictionary with keys=nodes\n", "nx.set_node_attributes(my_graph, centralities, \"eigenvector\")\n", "\n", "# Now we can refer to the node's attributes in the graph\n", "print(my_graph.nodes[3][\"eigenvector\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "vNKEl9Zo41bY" }, "source": [ "We want to do this so that we can export our graph as a `gexf` file using networkx's [write_gexf](https://networkx.org/documentation/stable/reference/readwrite/generated/networkx.readwrite.gexf.write_gexf.html?highlight=write_gexf#networkx.readwrite.gexf.write_gexf) function. Gexf is able to contain a lot more information than other graph datatypes like pajek. It can contain information about the node attributes or edge attributes that belong to the graph and then these attributes will be recognized by Gephi for plotting.\n", "\n", "Alternatively, if you use Cytoscape, you can export the centralities as a node property file, which is just a CSV contains the node IDs as the first column, and centralities as the other columns. Cytoscape can read CSV files through \"important data tables\" functionality: https://manual.cytoscape.org/en/stable/Node_and_Edge_Column_Data.html\n", "\n", "Once the graph is saved and you open it in Gephi or cytoscape, you can use the node (or edge) attributes to control node (or edge) size and color. \n", "\n", "You can then arrange your nodes accordingly and then save separate visualizations that only change the node color/size according to your saved attributes. You will be using this ability for the following questions.\n", "\n", "**What to submit**: For this assignment, submit a PDF that contains your responses to the questions in this notebook, including visualizations for the following questions. You do not need to submit a copy of this notebook if all of your solutions are in the PDF. **Keep the node location the same** for your graph visualizations.\n", "\n", "## Picking the right Dolphins\n", "Answer the following questions:\n", "\n", "#### (1) Popularity contest\n", "We want to know who the top dolphins are in the network, the real centers of attraction. Using what you learned about centrality from the readings and videos, choose an appropriate centrality measure that will tell us who those dolphins are. Justify your decision and list who the important dolphins are.\n", "\n", "#### (2) Relay\n", "Dolphins like passing information around efficiently along the shortest-paths. Among their neighbors who are the most important message relayers in the network? Justify your centrality choice for finding these dolphins.\n", "\n", "#### (3) Gossip \n", "There is a lot smack going around the pod and everyone wants to know if Flipper will be inviting them to the party next week. But gossip takes time travel. Which dolphins are in the best position for getting all the best gossip from around the pod? Justify your centrality choice for finding these dolphins." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3v2DFnkh41bY" }, "outputs": [], "source": [ "# YOUR SOLUTION HERE" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "id": "oiro6DEd41bY" }, "outputs": [], "source": [ "nx.write_gexf(dolphin_social_network, \"dolphin_centrality.gexf\")" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "m06-exploring_centralities.ipynb", "provenance": [] }, "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": "3.11.5" }, "vscode": { "interpreter": { "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" } } }, "nbformat": 4, "nbformat_minor": 0 }