This paper investigates the performance of integrated sensing and communication (ISAC) networks, focusing on the balance between sensing and communication (S&C) performance at the network level. The authors propose a novel cooperative ISAC scheme that combines multipoint (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques. By leveraging stochastic geometry, they analyze the S&C performance, revealing key cooperative dependencies and optimizing network-level parameters. A significant finding is that deploying N ISAC transceivers improves average cooperative sensing performance according to the $ \ln^{2}N $ scaling law, which is less pronounced than the $ N^{2} $ performance gain when transceivers are equidistant from the target due to path loss effects. The paper also derives a tight expression for communication rate and presents a low-complexity algorithm to determine the optimal cooperative cluster size. The authors formulate an optimization problem to maximize network performance in terms of two joint S&C metrics, jointly optimizing cooperative BS cluster sizes and transmit power. Simulation results show that the proposed cooperative ISAC scheme outperforms conventional time-sharing and non-cooperative schemes in terms of average data rate and CRLB reduction, achieving a better S&C performance tradeoff. The study highlights the importance of balancing S&C performance gains with control signaling costs and provides insights into the network-level tradeoffs in ISAC systems.This paper investigates the performance of integrated sensing and communication (ISAC) networks, focusing on the balance between sensing and communication (S&C) performance at the network level. The authors propose a novel cooperative ISAC scheme that combines multipoint (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques. By leveraging stochastic geometry, they analyze the S&C performance, revealing key cooperative dependencies and optimizing network-level parameters. A significant finding is that deploying N ISAC transceivers improves average cooperative sensing performance according to the $ \ln^{2}N $ scaling law, which is less pronounced than the $ N^{2} $ performance gain when transceivers are equidistant from the target due to path loss effects. The paper also derives a tight expression for communication rate and presents a low-complexity algorithm to determine the optimal cooperative cluster size. The authors formulate an optimization problem to maximize network performance in terms of two joint S&C metrics, jointly optimizing cooperative BS cluster sizes and transmit power. Simulation results show that the proposed cooperative ISAC scheme outperforms conventional time-sharing and non-cooperative schemes in terms of average data rate and CRLB reduction, achieving a better S&C performance tradeoff. The study highlights the importance of balancing S&C performance gains with control signaling costs and provides insights into the network-level tradeoffs in ISAC systems.