Advances in Network Analysis and Game Theory

The field of network analysis and game theory is rapidly evolving, with a focus on developing new models and algorithms to analyze and optimize complex networks. Recent research has explored the application of game theory to network disruption, public goods games, and influence blocking maximization. Notably, the development of new mathematical models, such as discounted cuts and coopetition indices, has enabled researchers to better understand and analyze complex network phenomena. Additionally, advances in algorithms for computing power indices, maximal independent sets, and perception gap minimization have improved our ability to analyze and optimize network behavior.

Some noteworthy papers in this area include: Discounted Cuts: A Stackelberg Approach to Network Disruption, which introduces a new mathematical model for analyzing network disruption and develops a unified algorithmic framework for solving discounted cut problems. Time-Critical Adversarial Influence Blocking Maximization, which proposes a new model and algorithm for influence blocking maximization in time-critical scenarios. Distributed MIS Algorithms for Rational Agents using Games, which develops new algorithms for computing maximal independent sets in distributed networks with rational agents.

Sources

Discounted Cuts: A Stackelberg Approach to Network Disruption

Optimal Welfare in Noncooperative Network Formation under Attack

Computing Equilibrium Nominations in Presidential Elections

R-enum Revisited: Speedup and Extension for Context-Sensitive Repeats and Net Frequencies

Public Goods Games in Directed Networks with Constraints on Sharing

PACE Solver Description: twin_width_fmi

Quantifying and Minimizing Perception Gap in Social Networks

Maximal Palindromes in MPC: Simple and Optimal

Computing Power Indices in Weighted Majority Games with Formal Power Series

Coopetitive Index: a measure of cooperation and competition in coalition formation

Time-Critical Adversarial Influence Blocking Maximization

Distributed MIS Algorithms for Rational Agents using Games

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