Extremal Properties of Complex Networks

We describe the structure of connected graphs with the minimum and maximum average distance, radius, diameter, betweenness centrality, efficiency and resistance distance, given their order and size. We find tight bounds on these graph qualities for any arbitrary number of nodes and edges and analytically derive the form and properties of such networks

Quantification and Minimization of Crosstalk Sensitivity in Networks

Crosstalk is defined as the set of unwanted interactions among the different entities of a network. Crosstalk is present in various degrees in every system where information is transmitted through a means that is accessible by all the individual units of the network. Using concepts from graph theory, we introduce a quantifiable measure for sensitivity to crosstalk, and analytically derive the structure of the networks in which it is minimized. It is shown that networks with an inhomogeneous degree distribution are more robust to crosstalk than corresponding homogeneous networks. We provide a method to construct the graph with the minimum possible sensitivity to crosstalk, given its order and size. Finally, for networks with a fixed degree sequence, we present an algorithm to find the optimal interconnection structure among their vertices

Network Structure Optimization with Applications to Minimizing Variance and Crosstalk

This thesis provides a unified methodology for analyzing structural properties of graphs, along with their applications. In the last several years, the field of complex networks has been extensively studied, and it is now well understood that the way a large network is built is closely intertwined with its function. Structural properties have an impact on the function of the network, and the form of many systems has been evolved in order to optimize for given functions. Despite the great progress, particularly in how structural attributes affect the various network functions, there is a significant gap in the quantitative study of how much these properties can change in a network without a significant impact on the functionality of the system, or what the bounds of these structural attributes are. Here, we find and analytically prove tight bounds of global graph properties, as well as the form of the graphs that achieve these bounds. The attributes studied include the network efficiency, radius, diameter, average distance, betweenness centrality, resistance distance, and average clustering. All of these qualities have a direct impact on the function of the network, and finding the graph that optimizes one or more of them is of interest when designing a large system. In addition, we measure how sensitive these properties are with respect to random rewirings or addition of new edges, since designing a network with a given set of constraints may include a lot of trade-offs. This thesis also studies properties that are of interest in both natural and engineered networks, such as maximum immunity to crosstalk interactions and random noise. We are primarily focused on networks where information is transmitted through a means that is accessible by all the individual units of the network and the interactions among the different entities that comprise it do not necessarily have a dedicated mechanism that facilitates information transmission, or isolates them from other parts of the network. Two examples of this class are biological and chemical reaction networks. Such networks suffer from unwanted crosstalk interactions when two or more units spuriously interact with each other. In addition, they are subject to random fluctuations in their output, both due to noisy inputs and because of the random variance of their parameters. These two types of randomness affect the behavior of the system in ways that are intrinsically different. We examine the network topologies that accentuate or alleviate the effect of random variance in the network for both directed and undirected graphs, and find that increasing the crosstalk among different parts reduces the output variance but also contributes to a slower response