No Time to Observe: Adaptive Influence Maximization with Partial Feedback

Yuan, Jing; Tang, Shaojie

doi:10.24963/ijcai.2017/546

Cited by 35 publications

(17 citation statements)

References 11 publications

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“…Recently, there is another type of work called adaptive IM [25][26][27][28][29], which has attracted many researchers' attention. These works assume that the feedback in the real-world is available.…”

Section: Influence Maximization In Static Networkmentioning

confidence: 99%

Maximizing Influence Over Streaming Graphs with Query Sequence

Zhao

Yuan

et al. 2021

Data Sci. Eng.

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Now, with the prevalence of social media, such as Facebook, Weibo, how to maximize influence of individuals, products, actions in new media is of practical significance. Generally, maximizing influence first needs to identify the most influential individuals since they can spread their influence to most of others in the social media. Many studies on influence maximization aimed to select a subset of nodes in static graphs once. Actually, real graphs are evolving. So, influential individuals are also changing. In these scenarios, people tend to select influential individuals multiple times instead of once. Namely, selections are raised sequentially, forming a sequence (query sequence). It raises several new challenges due to changing influential individuals. In this paper, we explore the problem of Influence Maximization over Streaming Graph (SGIM). Then, we design a compact solution for storing and indexing streaming graphs and influential nodes that eliminates the redundant computation. The solution includes Influence-Increment-Index along with two sketch-centralized indices called Influence-Index and Reverse-Influence-Index. Computing influence set of nodes will incur a large number of redundant computations. So, these indices are designed to keep track of the nodes’ influence in sketches. Finally, with the indexing scheme, we present the algorithm to answer SGIM queries. Extensive experiments on several real-world datasets demonstrate that our method is competitive in terms of both efficiency and effectiveness owing to the design of index.

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Section: Influence Maximization In Static Networkmentioning

confidence: 99%

Maximizing Influence Over Streaming Graphs with Query Sequence

Zhao

Yuan

et al. 2021

Data Sci. Eng.

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“…Chen and Krause (2013) propose a policy that selects batches of fixed size r, and they show that their policy achieves a bounded approximation ratio compare to the optimal policy which is restricted to selecting batches of fixed size r. However, their approximation ratio becomes arbitrarily bad with respect to the optimal fully adaptive policy. In the context of adaptive viral marketing, Yuan and Tang (2017) develop a partial-adaptive seeding policy that achieves a bounded approximation ratio against the optimal fully adaptive seeding policy. Our study is similar to theirs in that both studies introduce a controlling parameter to balance the performance/adaptivility tradeoff.…”

Section: Related Workmentioning

confidence: 99%

Partial-Adaptive Submodular Maximization

Tang¹,

Yuan²

2021

Preprint

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The goal of a typical adaptive sequential decision making problem is to design an interactive policy that selects a group of items sequentially, based on some partial observations, to maximize the expected utility.It has been shown that the utility functions of many real-world applications, including pooled-based active learning and adaptive influence maximization, satisfy the property of adaptive submodularity. However, most of existing studies on adaptive submodular maximization focus on the fully adaptive setting, i.e., one must wait for the feedback from all past selections before making the next selection. Although this approach can take full advantage of feedback from the past to make informed decisions, it may take a longer time to complete the selection process as compared with the non-adaptive solution where all selections are made in advance before any observations take place. In this paper, we explore the problem of partial-adaptive submodular maximization where one is allowed to make multiple selections in a batch simultaneously and observe their realizations together. Our approach enjoys the benefits of adaptivity while reducing the time spent on waiting for the observations from past selections. To the best of our knowledge, no results are known for partial-adaptive policies for the non-monotone adaptive submodular maximization problem. We study this problem under both cardinality constraint and knapsack constraints, and develop effective and efficient solutions for both cases. We also analyze the batch query complexity, i.e., the number of batches a policy takes to complete the selection process, of our policy under some additional assumptions.

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“…Submodular maximization has studied extensively recently due to its applications in a wide range of domains including active learning (Golovin and Krause 2011), virtual marketing Yuan 2020, Yuan andTang 2017b), sensor placement (Krause and Guestrin 2007). Under the non-adaptive setting, where the state of each item is deterministic, Nemhauser et al (1978) show that a classic greedy algorithm achieves a 1 − 1/e approximation ratio when maximizing a monotone submodular function subject to a cardinality constraint.…”

Section: Introductionmentioning

confidence: 99%

Beyond pointwise submodularity: Non-monotone adaptive submodular maximization in linear time

Tang

2021

Theoretical Computer Science

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In this paper, we study the non-monotone adaptive submodular maximization problem subject to a knapsack constraint. The input of our problem is a set of items, where each item has a particular state drawn from a known prior distribution. However, the state of an item is initially unknown, one must select an item in order to reveal the state of that item. Moreover, each item has a fixed cost. There is a utility function which is defined over items and states. Our objective is to sequentially select a group of items to maximize the expected utility subject to a knapsack constraint. Although the cardinality-constrained, as well as the more general matroid-constrained, adaptive submodular maximization has been well studied in the literature, whether there exists a constant approximation solution for the knapsack-constrained adaptive submodular maximization problem remains an open problem. We fill this gap by proposing the first constant approximation solution. In particular, our main contribution is to develop a sampling-based randomized algorithm that achieves a 1 10 approximation for maximizing an adaptive submodular function subject to a knapsack constraint.

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No Time to Observe: Adaptive Influence Maximization with Partial Feedback

Cited by 35 publications

References 11 publications

Maximizing Influence Over Streaming Graphs with Query Sequence

Maximizing Influence Over Streaming Graphs with Query Sequence

Partial-Adaptive Submodular Maximization

Beyond pointwise submodularity: Non-monotone adaptive submodular maximization in linear time

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