Algorithm for Evolutionary Games on Small-World Networks in Complex Networks
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In the realm of algorithms for evolutionary games on small-world networks within complex networks, numerous research-worthy challenges exist. For instance, developing more precise small-world network models using algorithms like Watts-Strogatz model implementation with tunable parameters (e.g., rewiring probability and network density) to better simulate real-world human interaction patterns; designing efficient algorithms incorporating strategy optimization techniques such as genetic algorithms or reinforcement learning to identify optimal strategies within small-world network topologies; and investigating social clustering phenomena through community detection algorithms like modularity maximization or label propagation to deepen understanding of societal operational mechanisms. These research directions hold significant value and warrant continued in-depth exploration.
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