Skip to main content

linkpred, a Python package for link prediction

Project description

⚠️ Note: This package is in maintenance mode. Critical bugs will continue to be resolved, but no new features will be implemented (more information).

Linkpred

Linkpred is a Python package for link prediction: given a network, Linkpred provides a number of heuristics (known as predictors) that assess the likelihood of potential links in a future snapshot of the network.

While some predictors are fairly straightforward (e.g., if two people have a large number of mutual friends, it seems likely that eventually they will meet and become friends), others are more involved.

https://codecov.io/gh/rafguns/linkpred/branch/master/graph/badge.svg?token=JVZIVHWJXY

linkpred can both be used as a command-line tool and as a Python library in your own code.

Installation

linkpred (v0.6 and later) works under Python 3.8 to 3.12. It depends on:

  • matplotlib

  • networkx

  • numpy

  • pyyaml

  • scipy

  • smokesignal

You should be able to install Linkpred and its dependencies using pip (pip install linkpred or python -m pip install linkpred). If you do not yet have Python installed, I recommend starting with Anaconda, which includes optimized versions of packages like numpy. If you want to use the Community predictor, which relies on community structure of the network, make sure you also have the python-louvain package by installing with pip install linkpred[community].

Example usage as command-line tool

A good starting point is linkpred --help, which lists all the available options. To save the predictions of the CommonNeighbours predictor, for instance, run:

$ linkpred examples/inf1990-2004.net -p CommonNeighbours --output cache-predictions

where examples/inf1990-2004.net is a network file in Pajek format. Other supported formats include GML and GraphML. The full output looks like this:

$ linkpred examples/inf1990-2004.net -p CommonNeighbours --output cache-predictions
16:43:13 - INFO - Reading file 'examples/inf1990-2004.net'...
16:43:13 - INFO - Successfully read file.
16:43:13 - INFO - Starting preprocessing...
16:43:13 - INFO - Removed 35 nodes (degree < 1)
16:43:13 - INFO - Finished preprocessing.
16:43:13 - INFO - Executing CommonNeighbours...
16:43:14 - INFO - Finished executing CommonNeighbours.
16:43:14 - INFO - Prediction run finished

$ head examples/inf1990-2004-CommonNeighbours-predictions_2016-04-22_16.43.txt
"Ikogami, K"    "Ikegami, K"    5.0
"Durand, T"     "Abd El Kader, M"       5.0
"Sharma, L"     "Kumar, S"      4.0
"Paul, A"       "Durand, T"     4.0
"Paul, A"       "Dudognon, G"   4.0
"Paul, A"       "Abd El Kader, M"       4.0
"Karisiddippa, CR"      "Garg, KC"      4.0
"Wu, YS"        "Kretschmer, H" 3.0
"Veugelers, R"  "Deleus, F"     3.0
"Veugelers, R"  "Andries, P"    3.0

Example usage within Python

>>> import linkpred
>>> G = linkpred.read_network("examples/training.net")
11:49:00 - INFO - Reading file 'examples/training.net'...
11:49:00 - INFO - Successfully read file.
>>> len(G)   # number of nodes
632
>>> # We exclude edges already present, to predict only new links
>>> simrank = linkpred.predictors.SimRank(G, excluded=G.edges())
>>> simrank_results = simrank.predict(c=0.5)
>>> top = simrank_results.top(5)
>>> for authors, score in top.items():
...    print(authors, score)
...
Tomizawa, H - Fujigaki, Y 0.188686630053
Shirabe, M - Hayashi, T 0.143866427916
Garfield, E - Fuseler, EA 0.148097050146
Persson, O - Larsen, IM 0.138516589957
Vanleeuwen, TN - Noyons, ECM 0.185040358711

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

linkpred-0.6.tar.gz (23.8 kB view details)

Uploaded Source

Built Distribution

linkpred-0.6-py3-none-any.whl (28.7 kB view details)

Uploaded Python 3

File details

Details for the file linkpred-0.6.tar.gz.

File metadata

  • Download URL: linkpred-0.6.tar.gz
  • Upload date:
  • Size: 23.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for linkpred-0.6.tar.gz
Algorithm Hash digest
SHA256 e9572aa3d265fc01a9f5e98826bc3e217e816700aba2cb6a10c7e875a7776c91
MD5 bb616eb2e0f316e996584ff0e08ec3ed
BLAKE2b-256 e2d99ad57f389434a4da2997d59d5bc843ba3a29e8cb7e38232fab1e8e2a5aa1

See more details on using hashes here.

Provenance

The following attestation bundles were made for linkpred-0.6.tar.gz:

Publisher: publish-to-pypi.yml on rafguns/linkpred

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file linkpred-0.6-py3-none-any.whl.

File metadata

  • Download URL: linkpred-0.6-py3-none-any.whl
  • Upload date:
  • Size: 28.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for linkpred-0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 12744bde8d0b55ac31f5c69b054eccf0f67f00206313cb275bbb5b978c2005d4
MD5 80789631caa770f8db07a6c9ff5aa5b1
BLAKE2b-256 921a6e7c1d4849b809a9e34fb7b52b084d950efa04a88747907204ef78eb47ba

See more details on using hashes here.

Provenance

The following attestation bundles were made for linkpred-0.6-py3-none-any.whl:

Publisher: publish-to-pypi.yml on rafguns/linkpred

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page