Effective and Efficient Similarity Index for Link Prediction of Complex Networks

TL;DR

This paper introduces a local path index for link prediction in complex networks, demonstrating high effectiveness and efficiency compared to existing methods, suitable for large-scale data mining.

Contribution

The paper proposes a new local path index that balances prediction accuracy with computational efficiency, outperforming traditional indices in large networks.

Findings

The local path index achieves comparable accuracy to the Katz index.

It requires significantly less CPU time and memory.

Effective on both modeled and real-world networks.

Abstract

Predictions of missing links of incomplete networks like protein-protein interaction networks or very likely but not yet existent links in evolutionary networks like friendship networks in web society can be considered as a guideline for further experiments or valuable information for web users. In this paper, we introduce a local path index to estimate the likelihood of the existence of a link between two nodes. We propose a network model with controllable density and noise strength in generating links, as well as collect data of six real networks. Extensive numerical simulations on both modeled networks and real networks demonstrated the high effectiveness and efficiency of the local path index compared with two well-known and widely used indices, the common neighbors and the Katz index. Indeed, the local path index provides competitively accurate predictions as the Katz index while requires much less CPU time and memory space, which is therefore a strong candidate for potential practical applications in data mining of huge-size networks.