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Bibliographic Details
Main Author:	Tuna, Ata
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Social and Information Networks
Online Access:	https://arxiv.org/abs/2503.01999
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author	Tuna, Ata
author_facet	Tuna, Ata
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2503_01999
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	A Deep Autoregressive Model for Dynamic Combinatorial Complexes Tuna, Ata Machine Learning Social and Information Networks 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.
title	A Deep Autoregressive Model for Dynamic Combinatorial Complexes
topic	Machine Learning Social and Information Networks
url	https://arxiv.org/abs/2503.01999

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