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Researchers reveal graph bandits for maximizing local influence in networks

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new approach called BARE for graph bandit problems, aiming to identify the most influential node in a network with minimal information requests. This method is particularly applicable to marketing in social networks, where the goal is to find and leverage key customers. Unlike previous methods that require partial or full graph knowledge, BARE operates without prior information, discovering the graph sequentially and actively. The proposed strategy offers a regret guarantee that scales with the detectable dimension, a quantity often smaller than the total number of nodes. AI

IMPACT Introduces a novel algorithm for influence maximization in unknown networks, potentially improving targeted marketing strategies.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for graph bandit problems.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Researchers reveal graph bandits for maximizing local influence in networks

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The cluster contains an academic paper detailing a new algorithm for graph bandit problems.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandra Carpentier, Michal Valko ·

    Revealing graph bandits for maximizing local influence

    arXiv:2605.00489v1 Announce Type: new Abstract: We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information from the graph as possible. One of the relevant applications for this setting i…

  2. arXiv cs.LG TIER_1 English(EN) · Michal Valko ·

    Revealing graph bandits for maximizing local influence

    We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information from the graph as possible. One of the relevant applications for this setting is marketing in social networks, where the market…

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