首个生成动态组合复合体的深度自回归模型，适用于社交网络与生物系统。
A Deep Autoregressive Model for Dynamic Combinatorial Complexes
- 采用自回归框架预测组合复合体随时间的演化过程。
- 在真实与合成数据上有效捕捉时序与高阶依赖关系。
- 适合研究动态复杂网络的学者，尤其关注高阶交互建模者。
我们提出DAMCC（动态组合复合体的深度自回归模型），首个专为生成动态组合复合体（CCs）设计的深度学习模型。与传统图模型不同，CCs能刻画高阶相互作用，适用于社交网络、生物系统和演化基础设施的建模。现有模型多聚焦静态图结构，而DAMCC解决了动态网络中时序演化与高阶结构建模的挑战。该模型采用自回归框架，预测复合体随时间的演变。通过在真实世界与合成数据集上的全面实验，验证了其对时序与高阶依赖的捕捉能力。作为同类首例，DAMCC为未来动态组合复合体建模的发展奠定基础，具备在更大规模网络上提升可扩展性与效率的潜力。
原文摘要 · Abstract (English)
We introduce DAMCC (Deep Autoregressive Model for Dynamic Combinatorial Complexes), the first deep learning model designed to generate dynamic combinatorial complexes (CCs). Unlike traditional graph-based models, CCs capture higher-order interactions, making them ideal for representing social networks, biological systems, and evolving infrastructures. While existing models primarily focus on static graphs, DAMCC addresses the challenge of modeling temporal dynamics and higher-order structures in dynamic networks. DAMCC employs an autoregressive framework to predict the evolution of CCs over time. Through comprehensive experiments on real-world and synthetic datasets, we demonstrate its ability to capture both temporal and higher-order dependencies. As the first model of its kind, DAMCC lays the foundation for future advancements in dynamic combinatorial complex modeling, with opportunities for improved scalability and efficiency on larger networks.
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