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LinkPred: a high performance library for link prediction in complex networks.

Said Kerrache¹

¹Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Riyadh, Saudi Arabia.

Peerj. Computer Science

|June 4, 2021

Summary

Link prediction algorithms analyze network structures to forecast missing connections. This new library, LinkPred, unifies and scales these tools for easier network analysis.

Keywords:

Complex networks High performance computing Software library Graph embedding Link prediction

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Area of Science:

Network Science
Computer Science
Data Mining

Background:

Real-world networks (e.g., WWW, social networks) exhibit non-random topologies.
These structures allow for inferring undiscovered interactions through link prediction.
Existing link prediction algorithm implementations are fragmented, hindering usability.

Purpose of the Study:

To introduce LinkPred, a unified, high-performance library for link prediction.
To facilitate the use and comparison of various link prediction algorithms.
To enable analysis of large-scale networks with millions of nodes and edges.

Main Methods:

Development of a parallel and distributed library, LinkPred.
Inclusion of major link prediction algorithms from existing literature.
Implementation of a unified interface for user interaction and algorithm comparison.

Main Results:

LinkPred supports large-scale networks (millions of nodes/edges).
The library offers a consistent interface for diverse link prediction methods.
It enables efficient parallel and distributed computation for network analysis.

Conclusions:

LinkPred addresses the fragmentation of link prediction tools.
It empowers researchers and practitioners with a scalable, unified platform.
Facilitates advancements in understanding and predicting network interactions.