{"type":"ScholarlyArticle","title":"Seed Selection in the Heterogeneous Moran Process","authors":[{"name":"Petros Petsinis","slug":"petros-petsinis","given":"Petros","family":"Petsinis","affiliation":"Department of Computer Science , Aarhus University , Aarhus , Denmark"},{"name":"Andreas Pavlogiannis","slug":"andreas-pavlogiannis","given":"Andreas","family":"Pavlogiannis","affiliation":"Department of Computer Science , Aarhus University , Aarhus , Denmark"},{"name":"Josef Tkadlec","slug":"josef-tkadlec","given":"Josef","family":"Tkadlec","affiliation":"Computer Science Institute , Charles University , Prague , Czech Republic"},{"name":"Panagiotis Karras","slug":"panagiotis-karras","given":"Panagiotis","family":"Karras","affiliation":"Department of Computer Science , University of Copenhagen , Copenhagen , Denmark; Department of Computer Science , Aarhus University , Aarhus , Denmark"}],"abstract":"The Moran process is a classic stochastic process that models the rise and takeover of novel traits in network-structured populations. In biological terms, a set of mutants, each with fitness m ∈ (0, ∞) invade a population of residents with fitness 1. Each agent reproduces at a rate proportional to its fitness and each offspring replaces a random network neighbor. The process ends when the mutants either fixate (take over the whole population) or go extinct. The fixation probability measures the success of the invasion. To account for environmental heterogeneity, we study a generalization of the Standard process, called the Heterogeneous Moran process. Here, the fitness of each agent is determined both by its type (resident/mutant) and the node it occupies. We study the natural optimization problem of seed selection: given a budget k, which k agents should initiate the mutant invasion to maximize the fixation probability? We show that the problem is strongly inapproximable: it is NP-hard to distinguish between maximum fixation probability 0 and 1. We then focus on mutant-biased networks, where each node exhibits at least as large mutant fitness as resident fitness. We show that the problem remains NP-hard, but the fixation probability becomes submodular, and thus the optimization problem admits a greedy (1 − 1/e)-approximation. An experimental evaluation of the greedy algorithm along with various heuristics on real-world data sets corroborates our results.","doi":"10.24963/ijcai.2024/254","url":"https://ijcai.org/proceedings/2024/254","pdf":"https://ijcai.org/proceedings/2024/0254.pdf","markdown":"https://ijcai.org/proceedings/2024/254.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/254","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":254,"pages":{"first":2297,"last":2305},"keywords":["Data Mining: DM: Networks","Agent-based and Multi-agent Systems: MAS: Resource allocation","Constraint Satisfaction and Optimization: CSO: Constraint optimization problems","Search: S: Evolutionary computation"],"videos":[],"references":[{"position":1,"raw":"[Adlam et al., 2015] Ben Adlam, Krishnendu Chatterjee, and Martin A Nowak. 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