Dynamic Homophily with Imperfect Recall: Modeling Resilience in Adversarial Networks
Saad Alqithami

TL;DR
This paper introduces a new framework integrating memory decay into homophily models, demonstrating that strategic forgetting enhances network resilience against adversarial disruptions across various graph structures.
Contribution
It develops a novel model combining memory decay with homophily, systematically evaluating its impact on network resilience under adversarial conditions.
Findings
Cosine similarity improves stability by up to 30% in sparse networks.
Strategic forgetting balances robustness and adaptability.
Memory-homophily alignment enhances resilience in adversarial settings.
Abstract
The purpose of this study is to investigate how homophily, memory constraints, and adversarial disruptions collectively shape the resilience and adaptability of complex networks. To achieve this, we develop a new framework that integrates explicit memory decay mechanisms into homophily-based models and systematically evaluate their performance across diverse graph structures and adversarial settings. Our methods involve extensive experimentation on synthetic datasets, where we vary decay functions, reconnection probabilities, and similarity measures, primarily comparing cosine similarity with traditional metrics such as Jaccard similarity and baseline edge weights. The results show that cosine similarity achieves up to a 30\% improvement in stability metrics in sparse, convex, and modular networks. Moreover, the refined value-of-recall metric demonstrates that strategic forgetting can…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques · Adversarial Robustness in Machine Learning
