nodes() method returns a list of nodes, while the. edges() methods that Eric went over in the video. Specifically, you're going to look for 'nodes of interest' and 'edges of interest'. Now that you know some basic properties of the graph and have practiced using NetworkX's drawing facilities to visualize components of it, it's time to explore how you can query it for nodes and edges. To access the last entry of T.edges(data=True), you can use list(T.edges(data=True)). What is the size of the graph T, the type of T.nodes(), and the data structure of the third element of the last edge listed in T.edges(data=True)? The len() and type() functions will be useful here. Wait for the IPython shell to indicate that the graph that has been preloaded under the variable name T (representing a Twitter network), and then answer the following question: You're now going to use the NetworkX API to explore some basic properties of the network, and are encouraged to experiment with the data in the IPython Shell. It is an anonymized Twitter network with metadata. The Twitter network comes from KONECT, and shows a snapshot of a subset of Twitter users. To get you up and running with the NetworkX API, we will run through some basic functions that let you query a Twitter network that has been pre-loaded for you and is available in the IPython Shell as T. Edges between nodes are represented as a tuple, in which each tuple shows the nodes that are present on that edge.īasics of NetworkX API, using Twitter network ¶.edges to add and see the edges present in the graph. nodes method to see the nodes present in the graph. The integers 1, 2, and 3 can be entered as nodes, using the add_nodes_from method, passing in the list, as an argument.Using nx.Graph(), we initialize an empty graph, to which we can add nodes and edges.This python library allows us to manipulate, analyze, and model, graph data.The friendship is represented as a line between two nodes, and may have metadata such as date, which represents the date we first met.The nodes may be 'Hugo' and myself, with metadata stored in a key-value pair as id and age.Lets say there are two friends, Hugo and myself, who met on May 21, 2016.Nodes and edges can have metadata associated with them.In mathematical terms, this is a graph.Networks are described by two sets of items, which form a 'network'.Leverage the network structure to find communities in the network.You can start to think about optimizing transportation between cities.By modeling the data as a network, you can gain insight into what entities (or nodes) are important, such as broadcasters or influencers in a social network.Pathfinding: most efficient transportation path.Important entities: influencers in social networks.Networks are a useful tool for modeling relationships between entities.

In a transportation network, we're modeling the connectivity between locations, as determined by the roads or flight paths connection them.In a social network, we're modeling the relationship between people.

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