Abstract

Motivated by the problem of human-trafficking, where it is often observed that criminal organizations are linked and behave similarly over time, we introduce the problem of Coupled Clustering of Time-series and their underlying Network. The goal is to find tightly connected subgroups of nodes that also have similar node-specific time series (temporal—not necessarily structural—behavior). We formulate the problem as a coupled matrix factorization for the time series, combined with regularization for network smoothness. We propose CCTN, and an incrementally-updated counterpart, CCTN-inc, which efficiently handles network updates. Extensive experiments show that CCTN is up to 4x more accurate than baselines that consider graph structure or time series alone, and CCTN-inc is up to 55x faster than CCTN. As an application, we explore an exclusive database with millions of online ads on human trafficking …

Date

May 6, 2019

Authors

Yike Liu, Linhong Zhu, Pedro Szekely, Aram Galstyan, Danai Koutra

Book

Proceedings of the 2019 SIAM International Conference on Data Mining

Pages

531-539

Publisher

Society for Industrial and Applied Mathematics