PATHATTACK: Attacking Shortest Paths in Complex Networks

  • Benjamin A. Miller
  • , Zohair Shafi
  • , Wheeler Ruml
  • , Yevgeniy Vorobeychik
  • , Tina Eliassi-Rad
  • , Scott Alfeld

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inferring semantic distances between concepts on the World Wide Web. An adversary with the capability to perturb the graph might make the shortest path between two nodes route traffic through advantageous portions of the graph (e.g., a toll road he owns). In this paper, we introduce the Force Path Cut problem, in which there is a specific route the adversary wants to promote by removing a low-cost set of edges in the graph. We show that Force Path Cut is NP-complete. It can be recast as an instance of the Weighted Set Cover problem, enabling the use of approximation algorithms. The size of the universe for the set cover problem is potentially factorial in the number of nodes. To overcome this hurdle, we propose the PATHATTACK algorithm, which via constraint generation considers only a small subset of paths—at most 5% of the number of edges in 99% of our experiments. Across a diverse set of synthetic and real networks, the linear programming formulation of Weighted Set Cover yields the optimal solution in over 98% of cases. We also demonstrate running time vs. cost tradeoff using two approximation algorithms and greedy baseline methods. This work expands the area of adversarial graph mining beyond recent work on node classification and embedding.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Proceedings
EditorsNuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, Jose A. Lozano
PublisherSpringer Science and Business Media Deutschland GmbH
Pages532-547
Number of pages16
ISBN (Print)9783030865191
DOIs
StatePublished - 2021
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: Sep 13 2021Sep 17 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12976 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
CityVirtual, Online
Period09/13/2109/17/21

Keywords

  • Adversarial graph perturbation
  • Constraint generation
  • Shortest path

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