Time Is of the Essence: Analyzing the Effect of Vertex-Joining Time on Complex Network Evolution

TL;DR

This paper analyzes how the timing of user joining influences social network evolution, using a large Reddit dataset to develop a new model that captures the impact of join time and rate on network topology.

Contribution

It introduces the Temporal Preferential Attachment model, incorporating join time and rate, to better simulate real-world complex network evolution.

Findings

Links form more among users who join simultaneously.

User join rate significantly affects network topology.

The new model produces more realistic, scale-free networks with high clustering.

Abstract

Complex networks have non-trivial characteristics and appear in many real-world systems. Such networks are vitally important, but their full underlying dynamics are not completely understood. Utilizing new data sources, however, can unveil the evolution process of these networks. This study uses the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus to date. We used this dataset to deeply examine the network evolution process, which resulted in two key observations: First, links are more likely to be created among users who join a network at a similar time. Second, the rate in which new users join a network is a central factor in molding a network's topology; i.e., different user-join patterns create different topological properties. Based on these observations, we developed the \textit{Temporal Preferential Attachment} random network generation model. This model produces not only scale-free networks that have relative high clustering coefficients, but also networks that are sensitive to both the rate and the time in which users join the network. This results in a more accurate and flexible model of how complex networks evolve, one which more closely represents real-world data.