Enhance Stability of Network by Edge Anchor

2025-07-15

AI Summary

This work addresses the problem of enhancing social network stability, formalized as “anchoring-based support reinforcement”: given a graph $ G $ and a budget $ b $, select $ b $ edges to anchor so as to maximize the total gain in truss number across all edges. As this problem is NP-hard, we propose the first edge-anchoring framework for optimizing support-number gain. Our method innovatively introduces upward-path constraints and dynamic support-degree verification, drastically reducing the search space. We design an efficient greedy algorithm that leverages a support-number classification tree and upward routing to compute global support changes induced by anchoring a single edge. Experiments on eight real-world networks demonstrate that our approach significantly outperforms baseline methods—achieving up to 12.6× speedup in computation and an average 23.4% improvement in stability gain—thereby effectively strengthening community robustness and user engagement.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Social mining and social search on the Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Identify influential relationships affecting community stability
• Maximize trussness gain by anchoring edges within budget
• Develop efficient algorithms for large-scale network reinforcement

Innovation

• Greedy framework for edge selection
• Upward-route method for trussness increment
• Classification tree minimizes redundant computations