Tracking Triadic Cardinality Distributions for Burst Detection in High-Speed Multigraph Streams

TL;DR

This paper introduces a novel method for detecting bursts in social networks by tracking changes in triadic cardinality distributions, which are robust against spam and efficiently estimated through sampling techniques.

Contribution

The authors propose a new burst detection approach based on triadic cardinality distributions and develop an efficient sampling framework for large-scale network analysis.

Findings

Triadic cardinality distributions change significantly during bursts.

The method is robust against social-bot spam attacks.

Sampling techniques effectively estimate the distributions in real-world networks.

Abstract

In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people's sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with sudden surges of user interactions in networks, which we call "bursts" in this work. We uncover that the emergence of a burst is accompanied with the formation of triangles in some properly defined networks. This finding motivates us to propose a new and robust method to detect bursts on OSNs. We first introduce a new measure, "triadic cardinality distribution", corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, in a network. We show that this distribution not only changes when a burst occurs, but it also has a robustness property that it is immunized against common spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can more reliably detect bursts than simply counting interactions on OSNs. To avoid handling massive activity data generated by OSN users during the triadic tracking, we design an efficient "sample-estimate" framework to provide maximum likelihood estimate on the triadic cardinality distribution. We propose several sampling methods, and provide insights about their performance difference, through both theoretical analysis and empirical experiments on real world networks.