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CD-Based Indices for Link Prediction in Complex Network.

Tao Wang¹, Hongjue Wang¹, Xiaoxia Wang²

¹School of Mathematics and Physics, North China Electric Power University, Baoding, Hebei Province, China.

|January 12, 2016

Summary

This summary is machine-generated.

A new cosine similarity index (CD) and local community density index (LD) improve link prediction accuracy in networks. Novel CD-based indices, like CD-LD-k, show robust performance across various network types.

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Area of Science:

Network Science
Data Mining
Computational Social Science

Background:

Link prediction is crucial for understanding network evolution and function.
Existing similarity-based algorithms face challenges in improving prediction accuracy.
Network properties like clustering and assortativity influence prediction performance.

Purpose of the Study:

To propose a novel cosine similarity index (CD) for link prediction.
To introduce a local community density index (LD).
To develop and evaluate a series of CD-based indices (CD-LD-k, CD*LD-k, CD-k, CDI) on real-world networks.

Main Methods:

Constructing node coordinate matrices from node distances.
Calculating cosine similarity between node coordinate vectors.
Integrating local community density (LD) with cosine similarity (CD).
Developing a refined index (CDI) for negative assortative networks.

Main Results:

CD-based indices, particularly CD-LD-k and CD*LD-k, demonstrate significant improvements in link prediction accuracy.
CD-LD-k and CD*LD-k exhibit robustness and excellent average performance regardless of network assortativity.
The CDI index enhances prediction accuracy specifically for negative assortative networks.

Conclusions:

The proposed CD and LD indices offer effective approaches for link prediction.
CD-LD-k and CD*LD-k provide reliable performance across diverse network structures.
The CDI index addresses limitations in predicting links within negative assortative networks.