Search Engine Drives the Evolution of Social Networks

TL;DR

This paper models how search engines influence social network evolution, showing they accelerate link formation and rumor spreading, with theoretical proofs and validation on real-world data.

Contribution

It introduces a quantitative model of social network evolution driven by search engines, including theoretical analysis and empirical validation.

Findings

Degree distribution follows an intensified power-law.

Network diameter shrinks over time.

Search engines significantly speed up rumor propagation.

Abstract

The search engine is tightly coupled with social networks and is primarily designed for users to acquire interested information. Specifically, the search engine assists the information dissemination for social networks, i.e., enabling users to access interested contents with keywords-searching and promoting the process of contents-transferring from the source users directly to potential interested users. Accompanying such processes, the social network evolves as new links emerge between users with common interests. However, there is no clear understanding of such a "chicken-and-egg" problem, namely, new links encourage more social interactions, and vice versa. In this paper, we aim to quantitatively characterize the social network evolution phenomenon driven by a search engine. First, we propose a search network model for social network evolution. Second, we adopt two performance metrics, namely, degree distribution and network diameter. Theoretically, we prove that the degree distribution follows an intensified power-law, and the network diameter shrinks. Third, we quantitatively show that the search engine accelerates the rumor propagation in social networks. Finally, based on four real-world data sets (i.e., CDBLP, Facebook, Weibo Tweets, P2P), we verify our theoretical findings. Furthermore, we find that the search engine dramatically increases the speed of rumor propagation.