Influence Maximization with Bandits
This work addresses the challenge of selecting optimal seed users for viral marketing without requiring initial influence data, which is incremental as it builds on existing bandit methods but introduces more realistic feedback models.
The paper tackles the influence maximization problem by developing a combinatorial multi-armed bandit approach that estimates influence probabilities sequentially, eliminating the need for prior knowledge of these probabilities. It establishes performance bounds under edge-level and novel node-level feedback, and demonstrates efficiency on four real datasets with experimental results.
We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we have data that lets us estimate these influences. However, this information is typically not initially available or is difficult to obtain. To avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm that estimates the influence probabilities as we sequentially try different seed sets. We establish bounds on the performance of this procedure under the existing edge-level feedback as well as a novel and more realistic node-level feedback. Beyond our theoretical results, we describe a practical implementation and experimentally demonstrate its efficiency and effectiveness on four real datasets.