Efficient Algorithms for Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems

Основні поняття

The core message of this article is to study the computational complexity and develop efficient algorithms for finding nontrivial minimum fixed points in discrete dynamical systems, which is an important problem in modeling the spread of undesirable contagions and decision-making in networked games.

Анотація

The article focuses on the Nontrivial Minimum Fixed Point Existence (NMin-FPE) problem in synchronous discrete dynamical systems (SyDSs) with threshold-based local functions. The key highlights and insights are:

Formulation: The authors formally define the NMin-FPE problem, which aims to find a nontrivial fixed point (i.e., a fixed point with at least one state-1 vertex) with the minimum number of state-1 vertices.

Intractability: The authors establish strong inapproximability and parameterized complexity results for NMin-FPE. Specifically, they show that NMin-FPE cannot be approximated within a factor of n^(1-ε) for any constant ε > 0, unless P = NP. They also prove that NMin-FPE is W[1]-hard with respect to the Hamming weight of the fixed point.

Efficient Algorithms: The authors identify several special cases where NMin-FPE can be solved efficiently, such as when the system has constant-1 vertices, follows the progressive threshold model, or the underlying graph is a directed acyclic graph or a complete graph.

ILP Formulation: The authors provide an integer linear programming (ILP) formulation to optimally solve NMin-FPE for networks of reasonable sizes.

Heuristic Framework: For larger networks, the authors propose a general heuristic framework along with three greedy selection strategies that can be embedded into the framework to obtain good (but not necessarily optimal) solutions.

Experimental Evaluation: The authors conduct extensive experiments on real-world and synthetic networks to demonstrate the effectiveness of the proposed heuristics, which outperform baseline methods significantly despite the strong inapproximability of NMin-FPE.

Статистика

None.

Цитати

None.

Ключові висновки, отримані з

Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems

by Zirou Qiu,Ch... о arxiv.org 04-02-2024

https://arxiv.org/pdf/2301.04090.pdf

Глибші Запити

How can the proposed heuristic framework be extended to handle other types of local functions beyond threshold functions

To extend the proposed heuristic framework to handle other types of local functions beyond threshold functions, we can modify the ILP formulation to accommodate different types of local functions. For example, for local functions that are not based on thresholds, we can introduce additional constraints in the ILP that capture the specific rules governing the state transitions of vertices. This may involve incorporating different types of constraints that reflect the logic of the local functions, such as logical constraints or constraints based on specific mathematical functions.
Additionally, we can adapt the heuristic framework to incorporate different types of local functions by adjusting the way in which the heuristic evaluates and selects vertices to set to state 1. This may involve considering different criteria or metrics to determine the impact of setting a vertex to state 1 based on the specific characteristics of the local functions. By customizing the heuristic framework to the characteristics of the local functions, we can effectively handle a broader range of dynamical systems beyond threshold functions.

What are the potential applications of nontrivial minimum fixed points in real-world scenarios, and how can the insights from this work be leveraged to address practical challenges

Nontrivial minimum fixed points have various potential applications in real-world scenarios, particularly in the context of modeling the spread of contagions, information dissemination, and decision-making processes in networked systems.
One practical application of nontrivial minimum fixed points is in the design of control strategies for limiting the spread of undesirable contagions, such as misinformation or harmful rumors, in social networks. By identifying fixed points with a minimum number of affected vertices, strategies can be developed to target specific individuals or groups to prevent the widespread adoption of negative information.
Moreover, insights from this work can be leveraged in the development of intervention strategies for promoting positive behaviors or increasing the adoption of beneficial information in social networks. By identifying nontrivial minimum fixed points that represent convergence points with a small number of affected individuals, targeted interventions can be designed to influence the behavior of key individuals and drive positive outcomes in the network.
Furthermore, the concept of nontrivial minimum fixed points can be applied in the optimization of resource allocation strategies, such as identifying critical nodes or vertices in a network that, when influenced, can lead to the desired outcomes with minimal resource expenditure. By leveraging the insights from this work, decision-makers can optimize their interventions to achieve maximum impact with limited resources.

Are there any other structural parameters, beyond the Hamming weight and the number of vertices with thresholds greater than 1, that could lead to fixed parameter tractable algorithms for NMin-FPE

In addition to the Hamming weight and the number of vertices with thresholds greater than 1, there are other structural parameters that could potentially lead to fixed parameter tractable algorithms for NMin-FPE. One such parameter could be the degree of the vertices in the network. By considering the degree of the vertices as a parameter, algorithms could be designed to exploit the structural properties of the network, such as the connectivity and centrality of nodes, to efficiently identify nontrivial minimum fixed points.
Another structural parameter that could be explored is the clustering coefficient of the network. The clustering coefficient captures the degree to which nodes in a network tend to cluster together, indicating the presence of tightly knit communities or cliques. By incorporating the clustering coefficient as a parameter, algorithms could focus on identifying nontrivial minimum fixed points within highly clustered regions of the network, where the dynamics of contagion or information spread may exhibit distinct patterns.
By exploring and leveraging additional structural parameters of the network, researchers can potentially uncover new insights and develop tailored algorithms that exploit the specific characteristics of the network topology to efficiently solve the NMin-FPE problem.