Enhance Stability of Network by Edge Anchor

TL;DR

This paper addresses the problem of reinforcing network stability by strategically anchoring edges to maximize community cohesion, introducing novel algorithms and validation on real-world networks.

Contribution

It formulates the NP-hard edge anchoring problem, proposes a scalable greedy framework with novel strategies, and validates effectiveness through extensive experiments.

Findings

The proposed methods significantly improve network trussness.

Algorithms are efficient on large-scale real-world networks.

Edge anchoring enhances overall network stability.

Abstract

With the rapid growth of online social networks, strengthening their stability has emerged as a key research focus. This study aims to identify influential relationships that significantly impact community stability. In this paper, we introduce and explore the anchor trussness reinforcement problem to reinforce the overall user engagement of networks by anchoring some edges. Specifically, for a given graph $G$ and a budget $b$, we aim to identify $b$ edges whose anchoring maximizes the trussness gain, which is the cumulative increment of trussness across all edges in $G$. We establish the NP-hardness of the problem. To address this problem, we introduce a greedy framework that iteratively selects the current best edge. To scale for larger networks, we first propose an upward-route method to constrain potential trussness increment edges. Augmented with a support check strategy, this approach enables the efficient computation of the trussness gain for anchoring one edge. Then, we design a classification tree structure to minimize redundant computations in each iteration by organizing edges based on their trussness. We conduct extensive experiments on 8 real-world networks to validate the efficiency and effectiveness of the proposed model and methods.