This study investigates the feasibility of large-scale distributed networks. The core focus of our research is the impact of multi-hop communication on point-to-point capacity per user (\(C_{P2P}\)), which is crucial for the scalability of distributed networks. By employing mathematical analysis, we estimate \(C_{P2P}\) in a distributed network with symmetrically arranged nodes, accounting for the power-law distribution of interaction probability based on distance. Our findings reveal that the capacity bands achieved surpass existing benchmarks by approximately \(\sqrt{\ln(n)}\), depending on the power-law exponent \((\alpha)\) value. Additionally, we present a novel stochastic analysis to determine the power-law exponent from available statistical data on social interactions. Both the mathematical analysis and real-world statistical data on the realistic value of \((\alpha)\) consistently support the feasibility of very large-scale distributed networks for applications rooted in social interactions.