# Critical Edge Identification: A K-Truss Based Model

## TL;DR

This paper introduces a novel k-truss minimization problem to identify critical edges in social networks, aiming to maximize network destabilization through edge removal, supported by efficient algorithms and real-world experiments.

## Contribution

It formulates the NP-hard k-truss minimization problem and develops pruning and bounding strategies for efficient solution computation.

## Key findings

- Proposed effective algorithms for k-truss minimization.
- Demonstrated the approach's efficiency on real social networks.
- Validated the method's ability to identify critical network connections.

## Abstract

In a social network, the strength of relationships between users can significantly affect the stability of the network. In this paper, we use the k-truss model to measure the stability of a social network. To identify critical connections, we propose a novel problem, named k-truss minimization. Given a social network G and a budget b, it aims to find b edges for deletion which can lead to the maximum number of edge breaks in the k-truss of G. We show that the problem is NP-hard. To accelerate the computation, novel pruning rules are developed to reduce the candidate size. In addition, we propose an upper bound based strategy to further reduce the searching space. Comprehensive experiments are conducted over real social networks to demonstrate the efficiency and effectiveness of the proposed techniques.