Using Graph Theory to Analyze the Bitcoin Network
In this talk, we present how a random graph model can be used to estimate the performance of the Bitcoin network. Our work addresses the impact of key blockchain parameters on the overall performance of Bitcoin. Using graph theory, we can demonstrate that a minimum safety threshold for the average number of connections maintained per node. Our results also show the performance trade-off between the default number of connections per node, network bandwidth, and block size to compute the optimal block propagation delay over the network. Finally, we will show an overview of how we leverage graph theory to estimate the number of forks in Bitcoin and the decentralization degree of the network.
Kaiwen Zhang
Kaiwen Zhang (Member, IEEE) received the B.Sc. and M.Sc. degrees from McGill University, Montreal, and the Ph.D. degree from the University of Toronto. He was an Alexander von Humboldt Postdoctoral Fellow in computer science at TU Munich. He is currently a Professor with the Department of Software and IT Engineering, ÉTS. His research interests include blockchain technologies, publish/subscribe systems, massive multiplayer online games, performance modeling, and software-defined networking.