This folder contains jupyter notebooks, with examples from graph-theory.
If you're new to the field I would recommend to study (loosely) in the order below:
basic graph theory: An introduction to the fundamental terminology of graph-theory. Topics are:
nodes
edges
indegree
outdegree
has_path
distance path
is a subgraph
examples of existing graphs available for testing.
generating and visualising graphs: An introduction to making random xy graphs, grids and visualise them.
comparing graphs: An overview of methods for comparing graphs, such as:
topological sort
phase lines
graph-hash
flow_graph_hash
merkle-tree
solving search problems: An introduction to different methods for findings paths, including:
adjacency matrix
BFS
DFS
DFScan
bidi-BFS
TSP
[critical path method]
find loops
calculating statistics about graphs provides an overview of common analysis of graphs, such as:
components
has cycles
network size
is partite
degree of separation
solving transport problems provides tools for a wide range of problems where discrete transport is essential.
minmax
minsum
shortest_tree all pairs.
scheduling problem
traffic scheduling problem
jam solver
trans shipment problem (needs rewrite)
solving flow problems provides tools for solving a wide range of problems where continuous flow are central.
max flow
max flow min cut
min cost flow
all_simple_paths
all_paths
solving assignment problems provides tools for solving any kind of assignment problem.
assignment problem
wtap
representing systems as graphs provides a use case
for using graph as finite state machine.