# Dynamic Homophily with Imperfect Recall: Modeling Resilience in Adversarial Networks

arXiv: https://arxiv.org/abs/2512.12332 · HTML: https://mlsanity.com/paper/2512.12332/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 13 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://mlsanity.com/paper/1705.05742/) (2017)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Cooperating with Machines](https://mlsanity.com/paper/1703.06207/) (2017)
- [Team-maxmin equilibrium: efficiency bounds and algorithms](https://mlsanity.com/paper/1611.06134/) (2016)
- [Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs](https://mlsanity.com/paper/2308.10779/) (2023)
- [Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs](https://mlsanity.com/paper/2009.00163/) (2020)
- [THeGCN: Temporal Heterophilic Graph Convolutional Network](https://mlsanity.com/paper/2412.16435/) (2024)
- [Imperfect-Recall Abstractions with Bounds in Games](https://mlsanity.com/paper/1409.3302/) (2014)
- [Higher-order correlations reveal complex memory in temporal hypergraphs](https://mlsanity.com/paper/2303.09316/) (2023)
- [The Value of Recall in Extensive-Form Games](https://mlsanity.com/paper/2412.19659/) (2024)
- [Forming Compatible Teams in Signed Networks](https://mlsanity.com/paper/2001.03128/) (2020)

## Abstract (arXiv)

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 bolster resilience by balancing network robustness and adaptability. The findings underscore the critical importance of aligning memory and similarity parameters with the structural and adversarial dynamics of the network. By quantifying the tangible benefits of incorporating memory constraints into homophily-based analyses, this study offers actionable insights for optimizing real-world applications, including social systems, collaborative platforms, and cybersecurity contexts.