Detecting Aberrant Linking Behavior in Directed Networks

Dorit S. Hochbaum, Quico Spaen, Mark Velednitsky

2019

Abstract

Agents with aberrant behavior are commonplace in today’s networks. There are fake profiles in social media, malicious websites on the internet, and fake news sources that are prolific in spreading misinformation. The distinguishing characteristic of networks with aberrant agents is that normal agents rarely link to aberrant ones. Based on this manifested behavior, we propose a directed Markov Random Field (MRF) formulation for detecting aberrant agents. The formulation balances two objectives: to have as few links as possible from normal to aberrant agents, as well as to deviate minimally from prior information (if given). The MRF formulation is solved optimally and efficiently. We compare the optimal solution for the MRF formulation to existing algorithms, including PageRank, TrustRank, and AntiTrustRank. To assess the performance of these algorithms, we present a variant of the modularity clustering metric that overcomes the known shortcomings of modularity in directed graphs. We show that this new metric has desirable properties and prove that optimizing it is NP-hard. In an empirical experiment with twenty-three different datasets, we demonstrate that the MRF method outperforms the other detection algorithms.

Paper Citation

in Harvard Style

Hochbaum D., Spaen Q. and Velednitsky M. (2019). Detecting Aberrant Linking Behavior in Directed Networks. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 72-82. DOI: 10.5220/0008069600720082

in Bibtex Style

@conference{kdir19,
author={Dorit S. Hochbaum and Quico Spaen and Mark Velednitsky},
title={Detecting Aberrant Linking Behavior in Directed Networks},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={72-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008069600720082},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Detecting Aberrant Linking Behavior in Directed Networks
SN - 978-989-758-382-7
AU - Hochbaum D.
AU - Spaen Q.
AU - Velednitsky M.
PY - 2019
SP - 72
EP - 82
DO - 10.5220/0008069600720082
PB - SciTePress