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Commit 0cc7d18

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Add notes that correspond to Jupyter notebook
These notes are out of sync with the Jupyter notebook, so a bit of work will be needed to copy results from the Jupyter notebook over to this org-mode document.
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‎notes/network_outline.org‎

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@@ -68,7 +68,9 @@ There are four basic tasks for interacting with networks
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- Understanding how to use Matplotlib to visualize and plot results
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- Describe basic analysis to be done on networks
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* Working with Nodes and Objects
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* Creating Networks From Scratch and Manipulation
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** Working with Nodes and Objects
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| Method Command | Description |
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|--------------------------------+-----------------------------------------------|
@@ -77,24 +79,44 @@ There are four basic tasks for interacting with networks
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| ~nx.all_neighbors(G, node)~ | Return all neighbors of node in graph |
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| ~nx.non_neighbors(G, node)~ | Return non-neighbors of node in graph |
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| ~nx.common_neighbors(G, u, v)~ | Return common neighbors of two nodes in graph |
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| ~G.add_node(node)~ | Add node to graph object, G |
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| ~G.add_nodes_from(array)~ | Add array of nodes to graph object, G |
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#+BEGIN_SRC python
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# Create (empty) graph with no nodes and no edges
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G = nx.Graph()
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#+END_SRC
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DG = nx.DiGraph()
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* Creating Networks From Scratch and Manipulation
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# Add one node
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G.add_node("Michael")
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# Add multiple nodes
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G.add_nodes_from(["Andy", "Jim", "Angela"])
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# Look at nodes are in our object
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G.nodes()
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#+END_SRC
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** Working with Edges and Connections
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| Method Command | Description |
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|-------------------------+------------------------------------|
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| ~nx.edges(G)~ | Return edge view of edges |
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| ~nx.number_of_edges(G)~ | Return number of edges in graph |
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| ~nx.non_edges(G)~ | Return non-existent edges in graph |
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| Method Command | Description |
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|----------------------------+------------------------------------|
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| ~nx.edges(G)~ | Return edge view of edges |
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| ~nx.number_of_edges(G)~ | Return number of edges in graph |
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| ~nx.non_edges(G)~ | Return non-existent edges in graph |
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| ~G.add_edge(node1, node2)~ | Add edge between node1 and node2 |
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#+BEGIN_SRC python
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# Add edges and connections between nodes
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DG.add_edge("Michael", "Jim")
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# Or add a group of edge conections all at once
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DG.add_edges_from([("Michael", "Angela"), ("Michael", "Andy"), ("Angela", "Andy")])
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#+END_SRC
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** Node and Edge Attributes
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Nodes and edges can not only just exist, but can also take on values or *attributes*.
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| Type | Method Command | Description |
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|------+-------------------------------------+----------------------------------------------------|
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| Node | ~nx.set_node_attributes(G, values)~ | Set node attributes from given value or dictionary |
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** Drawing Graphs in Different Ways
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There are lots of flexibility in drawing networks. You can find more
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There is lots of flexibility in drawing networks. You can find more
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[[https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#networkx.drawing.nx_pylab.draw_networkx][here in the ~draw_networkx()~ reference documentation]].
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*** Drawing Algorithms
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While it is great to create and have a network in a data structure, it is also
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important and interesting to explore these networks visually by plotting them
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in various way.
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in various ways.
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| Method Command | Description |
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| Method Command | Drawing Description |
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|-----------------------+------------------------------------------------------------------------|
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| ~nx.draw(G)~ | Draw graph with Matplotlib |
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| ~nx.draw_random(G)~ | Draw graph with random layout |
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#+BEGIN_SRC python
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# Use the *_layout() functions within the draw() method
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nx.draw(KG, pos=spring_layout(KG))
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nx.draw(KG, pos=nx.spring_layout(KG))
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#+END_SRC
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*** Drawing Nodes
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There are a few paramters for the ~nx.draw()~ method that can manipulate node
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There are a few parameters for the ~nx.draw()~ method that can manipulate node
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properties.
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| Paramter | Description |
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Here are some parameters specific for modifying how edges are displayed.
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| Paramter | Description |
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| Parameter | Description |
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|--------------+-----------------------------------------------------------|
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| ~arrows~ | For directed graphs, if ~True~ draw arrowheads |
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| ~arrowstyle~ | For directed graphs, choose style of arrow |
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*** Degree of Nodes
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Remember, that there are two basic kinds of graphs: directed and undirected. In
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the directed graph, there are two different kinds of edges: in and out.
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Remember, there are two basic kinds of graphs: directed and undirected. In the
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directed graph, there are two different kinds of edges: in and out.
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#+BEGIN_SRC python
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# Find in-degree edges
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|------------------------+------------------------------------------------------+--------------------------------|
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| Degree centrality | Node is important based on degrees | ~nx.degree_centrality(G)~ |
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| Eigenvector centrality | Node is important if linked by other important nodes | ~nx.eigenvector_centrality(G)~ |
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| Betweenness centrality | Node is important if within shortest paths in nodes | ~nx.betweeneess_centrality(G)~ |
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| Betweenness centrality | Node is important if within shortest paths in nodes | ~nx.betweenness_centrality(G)~ |
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#+BEGIN_SRC python
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# Calculate eigenvector centrality
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nx.eigenvector_centrality(DG)
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nx.eigenvector_centrality(KG)
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#+END_SRC
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** Basic Graph Algorithms
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network for particular nodes or certain paths. A path is a sequence of edges
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between two nodes.
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*Situation*: social networks like to recommend friends to you and one idea is to
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find friends close enough to your current social network, who else would you
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know?
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*Problem*: Social networks like to recommend friends to you and one idea is to
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find friends close enough to your current social network. So who else would you
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potentially know?
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#+BEGIN_SRC python
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# Check a path can exist
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*** Community Detection
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In a social network, you may be interested in tightly connected group of
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In a social network, you may be interested in tightly connected groups of
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friends. We can use a method called *community detection* or *module detection*
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to answer this question.
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*Bonus Challenge*: Create a function and script to use the random network from the
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previous exercise, creates 10 networks of size 5, 10, 15 and use your just
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created function to return a dictionary with the average for each
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created function to return a dictionary with the average number of neighbors
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for each.
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* Resources
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