Debunking the Myths of Influence Maximization

Arora, Akhil; Galhotra, Sainyam; Ranu, Sayan

doi:10.1145/3035918.3035924

Cited by 109 publications

(39 citation statements)

References 25 publications

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“…Golovin and Krause [18] show that the above greedy policy returns a (ln η + 1) 2 -approximate solution to the optimum. 1 This approximation guarantee, however, does not lead to a practical algorithm for the ASM problem, because (i) it requires the help from an oracle to exactly identify the node with the maximum expected marginal truncated spread in each round, but (ii) computing the exact expected spread of any node set is #P-hard [7].…”

Section: Existing Solutionsmentioning

confidence: 99%

“…Motivated by this observation, Vaswani and Lakshmanan [42] attempt to extend Golovin and Krause's method by replacing the oracle with an spread estimator with bounded errors. In particular, they assume that for any 1 Golovin and Krause claim that the approximation guarantee is (ln η + 1) in an earlier version of their work [17], but point out that the proof has gaps in a revised version [18].…”

Section: Existing Solutionsmentioning

confidence: 99%

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Efficient Approximation Algorithms for Adaptive Seed Minimization

Tang

Huang

Xiao

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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As a dual problem of influence maximization, the seed minimization problem asks for the minimum number of seed nodes to influence a required number η of users in a given social network G. Existing algorithms for seed minimization mostly consider the non-adaptive setting, where all seed nodes are selected in one batch without observing how they may influence other users.In this paper, we study seed minimization in the adaptive setting, where the seed nodes are selected in several batches, such that the choice of a batch may exploit information about the actual influence of the previous batches. We propose a novel algorithm, ASTI, which addresses the adaptive seed minimization problem in O * A short version of the

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Section: Existing Solutionsmentioning

confidence: 99%

Section: Existing Solutionsmentioning

confidence: 99%

Efficient Approximation Algorithms for Adaptive Seed Minimization

Tang

Huang

Xiao

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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show abstract

“…One particularly promising set of algorithms use the concept of reverse reachable (RR) sets to select seed nodes. These include the TIM, TIM+ [11] and IMM [12] algorithms, which are currently considered to be among the top-performing influence maximisation approaches on realistic benchmark problems [13].…”

Section: Related Workmentioning

confidence: 99%

Influence maximisation beyond organisational boundaries

Stein

Eshghi

Bellamy

et al. 2017

2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun

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Abstract-We consider the problem of choosing influential members within a social network, in order to disseminate a message as widely as possible. While this so-called problem of influence maximisation has been widely studied, little work considers partiallyobservable networks, where only part of a network is visible to the decision maker. Yet, this is critical in many applications, where an organisation needs to distribute its message far beyond its boundaries and beyond its usual sphere of influence. In this paper, we show that existing algorithms are not sufficient to handle such scenarios. To address this, we propose a set of novel adaptive algorithms that perform well in partially observable settings, achieving an up to 18% improvement on the non-adaptive state of the art.

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“…A rich literature followed, focusing on computationally efficient and scalable algorithms to solve IM. The recent benchmarking study of Arora et al [4] summarizes state-of-the-art techniques and also debunks many IM myths.…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, all the IM studies discussed in [4] have as starting point a specific diffusion model (IC or LT), whose graph topology and parameters -basically the edge weights -are known. In order to infer the diffusion parameters or the underlying graph structure, or both, [5], [6], [7], [8] propose offline, model-specific methods, which rely on observed information cascades.…”

Section: Introductionmentioning

confidence: 99%

Effective Large-Scale Online Influence Maximization

Lagrée

Cappé²,

Cautis³

et al. 2017

2017 IEEE International Conference on Data Mining (ICDM)

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Abstract-In this paper, we study a highly generic version of influence maximization (IM), one of optimizing influence campaigns by sequentially selecting "spread seeds" from a set of candidates, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain "persistently" active throughout and thus do not yield further rewards. We call this problem online influence maximization with persistence. We introduce an estimator on the candidates' missing mass -the expected number of nodes that can still be reached from a given seed candidate -and justify its strength to rapidly estimate the desired value. We then describe a novel algorithm, GT-UCB, relying on upper confidence bounds on the missing mass. We show that our approach leads to high-quality spreads on classic IM datasets, even though it makes almost no assumptions on the diffusion medium. Importantly, it is orders of magnitude faster than state-of-the-art IM methods.

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Debunking the Myths of Influence Maximization

Cited by 109 publications

References 25 publications

Efficient Approximation Algorithms for Adaptive Seed Minimization

Efficient Approximation Algorithms for Adaptive Seed Minimization

Influence maximisation beyond organisational boundaries

Effective Large-Scale Online Influence Maximization

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