Optimizing the Coherence of Composite Networks
This work provides theoretical and algorithmic tools for designing composite networks with improved performance, relevant to control and network science communities.
The paper addresses how to optimally connect disjoint networks to maximize coherence (an H2 norm measure) for consensus dynamics. For dynamics without stubborn agents, analytical expressions and optimal interconnection topologies are derived; for dynamics with stubborn agents, a non-combinatorial algorithm approximates the optimal composite graph.
We consider how to connect a set of disjoint networks to optimize the performance of the resulting composite network. We quantify this performance by the coherence of the composite network, which is defined by an $H_2$ norm of the system. Two dynamics are considered: noisy consensus dynamics with and without stubborn agents. For noisy consensus dynamics without stubborn agents, we derive analytical expressions for the coherence of composite networks in terms of the coherence of the individual networks and the structure of their interconnections. We also identify optimal interconnection topologies and give bounds on coherence for general composite graphs. For noisy consensus dynamics with stubborn agents, we develop a non-combinatorial algorithm that identifies connecting edges such that the composite network coherence closely approximates the performance of the optimal composite graph.