NEW: A Generic Learning Model for Tie Strength Prediction in Networks

TL;DR

This paper introduces a flexible, structure-based computational framework called NEW for predicting tie strength in various networks, outperforming existing methods especially with limited data.

Contribution

The paper presents a novel, generalizable model for tie strength prediction that relies solely on network structure, adaptable to diverse network types, and efficient for large-scale applications.

Findings

Outperforms state-of-the-art methods on six real-world networks.

Capable of predicting missing tie strengths with partial information.

Has linear time complexity, suitable for large networks.

Abstract

Tie strength prediction, sometimes named weight prediction, is vital in exploring the diversity of connectivity pattern emerged in networks. Due to the fundamental significance, it has drawn much attention in the field of network analysis and mining. Some related works appeared in recent years have significantly advanced our understanding of how to predict the strong and weak ties in the social networks. However, most of the proposed approaches are scenario-aware methods heavily depending on some special contexts and even exclusively used in social networks. As a result, they are less applicable to various kinds of networks. In contrast to the prior studies, here we propose a new computational framework called Neighborhood Estimating Weight (NEW) which is purely driven by the basic structure information of the network and has the flexibility for adapting to diverse types of networks. In NEW, we design a novel index, i.e., connection inclination, to generate the representative features of the network, which is capable of capturing the actual distribution of the tie strength. In order to obtain the optimized prediction results, we also propose a parameterized regression model which approximately has a linear time complexity and thus is readily extended to the implementation in large-scale networks. The experimental results on six real-world networks demonstrate that our proposed predictive model outperforms the state of the art methods, which is powerful for predicting the missing tie strengths when only a part of the network's tie strength information is available.