SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs

TL;DR

SliceNDice introduces a multi-view graph mining framework to detect suspicious multi-attribute entity groups, demonstrating high accuracy and scalability in real-world and simulated scenarios, advancing platform integrity efforts.

Contribution

It formulates suspicious group detection as a multi-view graph mining problem, proposes a novel suspiciousness metric, and develops an efficient algorithm validated on large-scale data.

Findings

Achieved 89% precision in real-world Snapchat data

Outperformed baselines with over 97% precision/recall in simulations

Demonstrated linear scalability of the algorithm

Abstract

Given the reach of web platforms, bad actors have considerable incentives to manipulate and defraud users at the expense of platform integrity. This has spurred research in numerous suspicious behavior detection tasks, including detection of sybil accounts, false information, and payment scams/fraud. In this paper, we draw the insight that many such initiatives can be tackled in a common framework by posing a detection task which seeks to find groups of entities which share too many properties with one another across multiple attributes (sybil accounts created at the same time and location, propaganda spreaders broadcasting articles with the same rhetoric and with similar reshares, etc.) Our work makes four core contributions: Firstly, we posit a novel formulation of this task as a multi-view graph mining problem, in which distinct views reflect distinct attribute similarities across entities, and contextual similarity and attribute importance are respected. Secondly, we propose a novel suspiciousness metric for scoring entity groups given the abnormality of their synchronicity across multiple views, which obeys intuitive desiderata that existing metrics do not. Finally, we propose the SliceNDice algorithm which enables efficient extraction of highly suspicious entity groups, and demonstrate its practicality in production, in terms of strong detection performance and discoveries on Snapchat's large advertiser ecosystem (89% precision and numerous discoveries of real fraud rings), marked outperformance of baselines (over 97% precision/recall in simulated settings) and linear scalability.