282 research outputs found
Revealing graph bandits for maximizing local influence
International audienceWe 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 marketer aims at finding and taking advantage of the most influential customers. The existing approaches for bandit problems on graphs require either partial or complete knowledge of the graph. In this paper, we do not assume any knowledge of the graph, but we consider a setting where it can be gradually discovered in a sequential and active way. At each round, the learner chooses a node of the graph and the only information it receives is a stochastic set of the nodes that the chosen node is currently influencing. To address this setting, we propose BARE, a bandit strategy for which we prove a regret guarantee that scales with the detectable dimension, a problem dependent quantity that is often much smaller than the number of nodes
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
We study the online influence maximization problem in social networks under
the independent cascade model. Specifically, we aim to learn the set of "best
influencers" in a social network online while repeatedly interacting with it.
We address the challenges of (i) combinatorial action space, since the number
of feasible influencer sets grows exponentially with the maximum number of
influencers, and (ii) limited feedback, since only the influenced portion of
the network is observed. Under a stochastic semi-bandit feedback, we propose
and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our
bounds on the cumulative regret are polynomial in all quantities of interest,
achieve near-optimal dependence on the number of interactions and reflect the
topology of the network and the activation probabilities of its edges, thereby
giving insights on the problem complexity. To the best of our knowledge, these
are the first such results. Our experiments show that in several representative
graph topologies, the regret of IMLinUCB scales as suggested by our upper
bounds. IMLinUCB permits linear generalization and thus is both statistically
and computationally suitable for large-scale problems. Our experiments also
show that IMLinUCB with linear generalization can lead to low regret in
real-world online influence maximization.Comment: Compared with the previous version, this version has fixed a mistake.
This version is also consistent with the NIPS camera-ready versio
Factorization Bandits for Online Influence Maximization
We study the problem of online influence maximization in social networks. In
this problem, a learner aims to identify the set of "best influencers" in a
network by interacting with it, i.e., repeatedly selecting seed nodes and
observing activation feedback in the network. We capitalize on an important
property of the influence maximization problem named network assortativity,
which is ignored by most existing works in online influence maximization. To
realize network assortativity, we factorize the activation probability on the
edges into latent factors on the corresponding nodes, including influence
factor on the giving nodes and susceptibility factor on the receiving nodes. We
propose an upper confidence bound based online learning solution to estimate
the latent factors, and therefore the activation probabilities. Considerable
regret reduction is achieved by our factorization based online influence
maximization algorithm. And extensive empirical evaluations on two real-world
networks showed the effectiveness of our proposed solution.Comment: 11 pages (including SUPPLEMENT
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