- Python Advanced Course Topics
- An Intro to Graph Theory
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- Creating Graphs in Python using Networkx
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Python Advanced Course TopicsAll NetworkX graph classes allow hashable Python objects as nodes and any Python object can be assigned as an edge attribute. NetworkX uses dicts to store the nodes and neighbors in a graph. So the reporting of nodes and edges for the base graph classes will not necessarily be consistent across versions and platforms. If you need the order of nodes and edges to be consistent e. In some algorithms it is convenient to temporarily morph a graph to exclude some nodes or edges. It should be better to do that via a view than to remove and then re-add. In other algorithms it is convenient to temporarily morph a graph to reverse directed edges, or treat a directed graph as undirected, etc. This module provides those graph views. The resulting views are essentially read-only graphs that report data from the orignal graph object. We provide an attribute G. Note: Since graphviews look like graphs, one can end up with view-of-view-of-view chains. Be careful with chains because they become very slow with about 15 nested views. For the common simple case of node induced subgraphs created from the graph class, we short-cut the chain by returning a subgraph of the original graph directly rather than a subgraph of a subgraph. We are careful not to disrupt any edge filter in the middle subgraph. Often it is easiest to use. View of G applying a filter on nodes and edges. View of G with edge directions reversed. NetworkX 2. The choice of graph class depends on the structure of the graph you want to represent. Which graph class should I use? Note NetworkX uses dicts to store the nodes and neighbors in a graph.
An Intro to Graph Theory
By convention None is not used as a node. If None default an empty graph is created. Add the nodes from any container a list, dict, set or even the lines from a file or the nodes from another graph. In addition to strings and integers any hashable Python object except None can represent a node, e. If some edges connect nodes not yet in the graph, the nodes are added automatically. There are no errors when adding nodes or edges that already exist. Warning: we protect the graph data structure by making G. However, you can assign to attributes in e. Often the best way to traverse all edges of a graph is via the neighbors. The neighbors are reported as an adjacency-dict G. Simple graph information is obtained using object-attributes and methods. Reporting usually provides views instead of containers to reduce memory usage. The views update as the graph is updated similarly to dict-views. The Graph class uses a dict-of-dict-of-dict data structure. Each of these three dicts can be replaced in a subclass by a user defined dict-like object. In general, the dict-like features should be maintained but extra features can be added. To replace one of the dicts create a new graph class by changing the class! Factory function to be used to create the dict containing node attributes, keyed by node id. It should require no arguments and return a dict-like object. Factory function to be used to create the node attribute dict which holds attribute values keyed by attribute name. Factory function to be used to create the outer-most dict in the data structure that holds adjacency info keyed by node. Factory function to be used to create the adjacency list dict which holds edge data keyed by neighbor.
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By definition, a Graph is a collection of nodes vertices along with identified pairs of nodes called edges, links, etc. In NetworkX, nodes can be any hashable object e. The graph G can be grown in several ways. NetworkX includes many graph generator functions and facilities to read and write graphs in many formats. You can add one node at a time. Node attributes are discussed further below. Note that G now contains the nodes of H as nodes of G. In contrast, you could use the graph H as a node in G. The graph G now contains H as a node. This flexibility is very powerful as it allows graphs of graphs, graphs of files, graphs of functions and much more. It is worth thinking about how to structure your application so that the nodes are useful entities. Of course you can always use a unique identifier in G and have a separate dictionary keyed by identifier to the node information if you prefer. G can also be grown by adding one edge at a time. An ebunch is any iterable container of edge-tuples. An edge-tuple can be a 2-tuple of nodes or a 3-tuple with 2 nodes followed by an edge attribute dictionary, e. Edge attributes are discussed further below. There are no complaints when adding existing nodes or edges. For example, after removing all nodes and edges. At this stage the graph G consists of 8 nodes and 3 edges, as can be seen by:. We can examine the nodes and edges. Four basic graph properties facilitate reporting: G. These are set-like views of the nodes, edges, neighbors adjacenciesand degrees of nodes in a graph. They offer a continually updated read-only view into the graph structure. They are also dict-like in that you can look up node and edge data attributes via the views and iterate with data attributes using methods. If you want a specific container type instead of a view, you can specify one. Here we use lists, though sets, dicts, tuples and other containers may be better in other contexts. One can specify to report the edges and degree from a subset of all nodes using an nbunch. An nbunch is any of: None meaning all nodesa node, or an iterable container of nodes that is not itself a node in the graph.
Creating Graphs in Python using Networkx