Revisiting Link Prediction: Evolving Models and Real Data Findings

TL;DR

This paper compares real social network data with generative models like Barabasi-Albert and Watts-Strogatz, highlighting the importance of clustering structures in link formation and suggesting improvements in network segmentation algorithms.

Contribution

It provides a comparative analysis of real network data with generative models, emphasizing the role of clustering in link prediction and proposing directions for better segmentation methods.

Findings

Watts-Strogatz networks yield more accurate link recommendations than Barabasi-Albert.

Clustering structure significantly influences link creation in real networks.

Enhanced segmentation algorithms are needed for large-scale network analysis.

Abstract

The explosive growth of Web 2.0, which was characterized by the creation of online social networks, has reignited the study of factors that could help us understand the growth and dynamism of these networks. Various generative network models have been proposed, including the Barabasi-Albert and Watts-Strogatz models. In this study, we revisit the problem from a perspective that seeks to compare results obtained from these generative models with those from real networks. To this end, we consider the dating network Skout Inc. An analysis is performed on the topological characteristics of the network that could explain the creation of new network links. Afterwards, the results are contrasted with those obtained from the Barabasi-Albert and Watts-Strogatz generative models. We conclude that a key factor that could explain the creation of links originates in its cluster structure, where link recommendations are more precise in Watts-Strogatz segmented networks than in Barabasi-Albert hierarchical networks. This result reinforces the need to establish more and better network segmentation algorithms that are capable of clustering large networks precisely and efficiently.