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1. Techniques for Blocking the Propagation of Two Simultaneous Contagions over Networks Using a Graph Dynamical Systems Framework

   https://doi.org/10.1017/nws.2022.18  

   Carscadden, Henry L.; Kuhlman, Chris J.; Marathe, Madhav V.; Ravi, S. S.; Rosenkrantz, Daniel J. (August 2022, Network science)

   We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations for the contagions. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. This optimization problem is NP-hard. We present two main solution approaches for the problem, namely an integer linear programming (ILP) formulation to obtain optimal solutions and a heuristic based on a generalization of the set cover problem. We carry out a comprehensive experimental evaluation of our solution approaches using many real-world networks. The experimental results show that our heuristic algorithm produces solutions that are close to the optimal solution and runs several orders of magnitude faster than the ILP-based approach for obtaining optimal solutions. We also carry out sensitivity studies of our heuristic algorithm. 

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2. Blocking the Propagation of Two Simultaneous Contagions over Networks

   Carscadden, Henry L; Kuhlman, Chris J; Marathe, Madhav V; Ravi, S S; Rosenkrantz, Daniel J (January 2020, International Conference on Complex Networks and their Applications (Complex Networks))

   null (Ed.)

   We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of infected nodes subject to a budget constraint on the total number of nodes that can be vaccinated. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. Since the optimization problem is NP-hard, we develop a heuristic based on a generalization of the set cover problem. Using experiments on three real-world networks, we compare the performance of the heuristic with some baseline methods. 

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3. Blocking the Propagation of Two Simultaneous Contagions over Networks

   Carscadden, H; Kuhlman, C; Marathe, M; Ravi, SS; Rosenkrantz, D (December 2020, International conference on complex networks and their applications)

   null (Ed.)

   We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of infected nodes subject to a budget constraint on the total number of nodes that can be vaccinated. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. Since the optimization problem is NP-hard, we develop a heuristic based on a generalization of the set cover problem. Using experiments on three real-world networks, we compare the performance of the heuristic with some baseline methods. 

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4. Toward scalable passive optically powered fronthaul networks

   https://doi.org/10.1364/JOCN.559900  

   Erbayat, Egemen; Figueiredo, Gustavo B; Petale, Shrinivas; Lin, Shih-Chun; Matsuura, Motoharu; Hasegawa, Hiroshi; Subramaniam, Suresh (January 2025, Journal of Optical Communications and Networking)

   The evolution of wireless networks toward 5G and beyond has introduced challenges in designing efficient and cost-effective fronthaul networks. This paper focuses on the power-enabled optical fronthaul design (POFD) problem, which seeks to optimize the placement of optical splitters and their connections to radio units and distributed units while incorporating power over fiber (PWoF) technology. PWoF technology offers significant advantages by enabling simultaneous data and power delivery over optical fibers, thus eliminating the need for separate power units and improving network resilience. However, the POFD problem is NP-hard, as the complexity of determining optimal splitter placements and interconnections grows exponentially with the network size. To address this complexity, we propose two comprehensive heuristic-based approaches and benchmark their performance against clustering-based methods such as k-means clustering and genetic algorithms. For small networks, where exact solutions can be computed using integer linear programming (ILP), we demonstrate that our heuristics achieve near-optimal results. For large networks, where ILP is computationally infeasible, our heuristics provide scalable and effective solutions and maintain high performance compared to existing clustering-based methods in the literature that do not guarantee feasible solutions. 

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5. Benchmarking framework for PON-based fronthaul network design

   https://doi.org/10.1364/JOCN.591752  

   Erbayat, Egemen; Figueiredo, Gustavo B; Lin, Shih-Chun; Matsuura, Motoharu; Hasegawa, Hiroshi; Subramaniam, Suresh (January 2026, Journal of Optical Communications and Networking)

   As mobile networks transition toward 5G and 6G RAN architectures, passive optical networks (PONs) offer a critical solution for cost-effective fronthaul transport. However, the lack of standardized evaluation models in the current literature makes an objective comparison of diverse optimization strategies difficult. This paper addresses this gap by proposing a unified benchmarking framework that standardizes cost catalogs and deployment scenarios. We formulate the network design problem using integer linear programming (ILP) to establish optimality bounds and evaluate three scalable heuristic strategies: a genetic algorithm, k-means clustering (KMC+), and a graph-based Randomized Successive Splitter Assignment (RSSA+) algorithm. Simulation results show that time-limited ILP remains a strong reference point, even when optimality is not reached. Despite being rarely used in prior fronthaul planning studies, it consistently yields solutions superior to those produced by standard heuristic methods. Among scalable approaches, RSSA+ reliably attains near-ILP performance while ensuring feasibility across all evaluated scenarios, which underscores the importance of advanced, constraint-aware algorithmic designs over simpler heuristics. 

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