Counting dominating sets and related structures in graphs

We consider some problems concerning the maximum number of (strong) dominating sets in a regular graph, and their weighted analogues. Our primary tool is Shearer's entropy lemma. These techniques extend to a reasonably broad class of graph parameters enumerating vertex colorings satisfying conditions on the multiset of colors appearing in (closed) neighborhoods. We also generalize further to enumeration problems for what we call existence homomorphisms. Here our results are substantially less complete, though we do solve some natural problems

The Number of Fixed Points of AND-OR Networks with Chain Topology

Boolean networks are sets of Boolean functions, which are functions that contain Boolean variables and the logical operators AND, OR, and NOT. In the simple case, the variables can be in one of two states—either 1 or 0, which can be interpreted in different ways such as ON or OFF, or TRUE or FALSE, depending on the application. Arranging model systems into Boolean functions, we can study steady states of these networks. This refers to the overall state of the dynamical system given an initial condition and another theoretical condition such as a subsequent point in time. Boolean networks have many applications, such as those in mathematics and computer science, and they can be used to study biological systems, especially to model gene networks. The wide range of applications for Boolean networks brings us to two important questions: how do we compute steady states, and how do we find the number of fixed points? Computing the number of fixed points is very difficult. One way to simplify the computation is to focus on certain classes of networks. Another way to simplify our scope is by focusing on certain network topologies. We focus on AND-OR networks with chain topology. AND-OR networks are Boolean networks where each coordinate function is either the AND or OR logical operator. We study the number of fixed points of these Boolean networks in the case that they have a wiring diagram with chain topology. We find closed formulas for subclasses of these networks and recursive formulas in the general case. Our results allow for an effective computation of the number of fixed points of AND-OR networks with chain topology. We further explore how our approach could be used in “fractal” chains

On the Existence and Design of the Best Stack Filter Based Associative Memory

The associative memory of a stack filter is defined to be the set of root signals of that filter. If the root sets of two stack filters both contain a desired set of patterns, but one filter’s root set is smaller than the other, then the filter with the smaller root set is said to be better for that set of patterns. Any filter which has the smallest number of roots containing the specified set of patterns is said to be a best filter. The configuration of the family of best filters is described via a graphical approach which specifies an upper and lower bound for the subset of possible best filters which are furthest from the sets of type-1 and type-2 stack filters. Knowledge of this configuration leads to an algorithm which can produce a near-best filter. This new method of constructing associative memories does not require the desired set of patterns to be independent and it can construct a much better filter than the methods in [I]

Voter and majority dynamics with biased and stubborn agents

We study binary opinion dynamics in a fully connected network of interacting agents. The agents are assumed to interact according to one of the following rules: (1) Voter rule: An updating agent simply copies the opinion of another randomly sampled agent; (2) Majority rule: An updating agent samples multiple agents and adopts the majority opinion in the selected group. We focus on the scenario where the agents are biased towards one of the opinions called the preferred opinion. Using suitably constructed branching processes, we show that under both rules the mean time to reach consensus is Θ(logN), where N is the number of agents in the network. Furthermore, under the majority rule model, we show that consensus can be achieved on the preferred opinion with high probability even if it is initially the opinion of the minority. We also study the majority rule model when stubborn agents with fixed opinions are present. We find that the stationary distribution of opinions in the network in the large system limit using mean field techniques

Mean Field Interactions in Heterogeneous Networks

In the context of complex networks, we often encounter systems in which the constituent entities randomly interact with each other as they evolve with time. Such random interactions can be described by Markov processes, constructed on suitable state spaces. For many practical systems (e.g. server farms, cloud data centers, social networks), the Markov processes, describing the time-evolution of their constituent entities, become analytically intractable as a result of the complex interdependence among the interacting entities. However, if the `strength' of these interactions converges to a constant as the size of the system is increased, then in the large system limit the underlying Markov process converges to a deterministic process, known as the mean field limit of the corresponding system. Thus, the mean field limit provides a deterministic approximation of the randomly evolving system. Such approximations are accurate for large system sizes. Most prior works on mean field techniques have analyzed systems in which the constituent entities are identical or homogeneous. In this dissertation, we use mean field techniques to analyze large complex systems composed of heterogeneous entities. First, we consider a class of large multi-server systems, that arise in the context of web-server farms and cloud data centers. In such systems, servers with heterogeneous capacities work in parallel to process incoming jobs or requests. We study schemes to assign the incoming jobs to the servers with the goal of achieving optimal performance in terms of certain metrics of interest while requiring the state information of only a small number of servers in the system. To this end, we consider randomized dynamic job assignment schemes which sample a small random subset of servers at every job arrival instant and assign the incoming job to one of the sampled servers based on their instantaneous states. We show that for heterogeneous systems, naive sampling of the servers may result in an `unstable' system. We propose schemes which maintain stability by suitably sampling the servers. The performances of these schemes are studied via the corresponding mean field limits, that are shown to exist. The existence and uniqueness of an asymptotically stable equilibrium point of the mean field is established in every case. Furthermore, it is shown that, in the large system limit, the servers become independent of each other and the stationary distribution of occupancy of each server can be obtained from the unique equilibrium point of the mean field. The stationary tail distribution of server occupancies is shown to have a fast decay rate which suggests significantly improved performance for the appropriate metrics relevant to the application. Numerical studies are presented which show that the proposed randomized dynamic schemes significantly outperform randomized static schemes where job assignments are made independently of the server states. In certain scenarios, the randomized dynamic schemes are observed to be nearly optimal in terms of their performances. Next, using mean field techniques, we study a different class of models that arise in the context of social networks. More specifically, we study the impact of social interactions on the dynamics of opinion formation in a social network consisting of a large number of interacting social agents. The agents are assumed to be mobile and hence do not have any fixed set of neighbors. Opinion of each agent is treated as a binary random variable, taking values in the set {0,1}. This represents scenarios, where the agents have to choose from two available options. The interactions between the agents are modeled using 1) the `voter' rule and 2) the `majority' based rule. Under each rule, we consider two scenarios, (1) where the agents are biased towards a specific opinion and (2) where the agents have different propensities to change their past opinions. For each of these scenarios, we characterize the equilibrium distribution of opinions in the network and the convergence rate to the equilibrium by analyzing the corresponding mean field limit. Our results show that the presence of biased agents can significantly reduce the rate of convergence to the equilibrium. It is also observed that, under the dynamics of the majority rule, the presence of `stubborn' agents (those who do not update their opinions) may result in a metastable network, where the opinion distribution of the non-stubborn agents fluctuates among multiple stable configurations

The number of fixed points of the majority rule

AbstractFormulae are derived for the number of cyclic binary strings of length n in which no single 1 occurs between two zeros and no single 0 occurs between two ones, and for the number of cyclic binary strings without substrings of the form 000 and 111. This problem is motivated by a problem of genetic information processing