NNetwork 0.1.0

Custom graph/network/multi-weighted network class based on storing list of neighbors for each nodes (as opposed to edge list) for scalable sampling and searching algorithms

NNetwork

NNetwork is a Custom graph/network/multi-weighted network class optimized for scalable sampling and searching algorithms. NNetwork stores a dictionary that maps each node to a list of its neighbors to allow for O(1) access for finding neighbors.

The efficiency of neighbor access is import for sampling algorithm such as random walks and glauber chain walks on graphs. Many packages rely on calculations involving powers of adjacency matrices to calculate random walks of length k, but Monte Carlo sampling algorithms rely on measurements made at every iteration of sampling.

By Josh Vendrow and Hanbaek Lyu

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Installation

To install NNetwork, run this command in your terminal:

$ pip install -U NNetwork

This is the preferred method to install NNetwork, as it will always install the most recent stable release.

If you don't have pip installed, these installation instructions can guide you through the process.

Usage

Undirected Graphs

Create a simple undirected graph from an edgelist:

>>> from NNetwork import NNetwork
>>> edgelist = [[1,2],[2,3],[3,4]]
>>> Network = NNetwork()
>>> Network.add_edges(edgelist)
>>> Network.has_edge(2,3)
True

Get the neighbors of a node:

>>> Network.neighbors(3)
[2,4]

Find the intersection of edges with another network:

>>> edgelist2 = [[2,3],[3,4],[5,7]]
>>> Network2 = NNetwork()
>>> Network2.add_edges(edgelist2)
>>> Network.intersection(Network2)
[[2,3],[3,4]]

Weighted Graphs

Create a weighted graph from an edgelist:

>>> from NNetwork import Wtd_NNetwork
>>> edgelist = [[1,2,0.5],[2,3,0.8]]]
>>> Network = NNetwork()
>>> Network.add_wtd_edges(edgelist)
>>> Network.get_edge_weight([2,3])
0.8

Convert weighted graph to an unweighed graph by thresholding

>>> Network_simple = Network.threshold2simple(0.7)
>>> Network_simple.edges()
[[2,3]]

Citing

If you use our work in an academic setting, please cite our paper:

Development

See CONTRIBUTING.md for information related to developing the code.

Suggested Git Branch Strategy

1. master is for the most up-to-date development, very rarely should you directly commit to this branch. Your day-to-day work should exist on branches separate from master. It is recommended to commit to development branches and make pull requests to master.4. It is recommended to use "Squash and Merge" commits when committing PR's. It makes each set of changes to master atomic and as a side effect naturally encourages small well defined PR's.

Additional Optional Setup Steps:

• Create an initial release to test.PyPI and PyPI.

  • Follow This PyPA tutorial, starting from the "Generating distribution archives" section.

• Create a blank github repository (without a README or .gitignore) and push the code to it.

• Delete these setup instructions from README.md when you are finished with them.