SCRank: Spammer and Celebrity Ranking in Directed Social Networks

TL;DR

SCRank is an iterative algorithm designed to identify celebrities and spammers in directed social networks, with proven convergence and validated effectiveness through experiments on real and synthetic data.

Contribution

The paper introduces SCRank, a novel algorithm for classifying users as celebrities or spammers in directed social networks, including a new convergence analysis method.

Findings

SCRank converges quickly and reliably on real and synthetic data.

The algorithm accurately identifies ground-truth celebrities and spammers in synthetic datasets.

Theoretical analysis guarantees convergence to an approximate equilibrium.

Abstract

Many online social networks allow directed edges: Alice can unilaterally add an "edge" to Bob, typically indicating interest in Bob or Bob's content, without Bob's permission or reciprocation. In directed social networks we observe the rise of two distinctive classes of users: celebrities who accrue unreciprocated incoming links, and follow spammers, who generate unreciprocated outgoing links. Identifying users in these two classes is important for abuse detection, user and content ranking, privacy choices, and other social network features. In this paper we develop SCRank, an iterative algorithm to identify such users. We analyze SCRank both theoretically and experimentally. The spammer-celebrity definition is not amenable to analysis using standard power iteration, so we develop a novel potential function argument to show convergence to an approximate equilibrium point for a class of algorithms including SCRank. We then use experimental evaluation on a real global-scale social network and on synthetically generated graphs to observe that the algorithm converges quickly and consistently. Using synthetic data with built-in ground truth, we also experimentally show that the algorithm provides a good approximation to planted celebrities and spammers.