Think globally, act locally: On the optimal seeding for nonsubmodular influence maximization

Schoenebeck, Grant; Tao, Biaoshuai; Yu, Fang-Yi

doi:10.1016/j.ic.2022.104919

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…Now, the worse-case scenario has accuracy 0.98 and rank 0.001%, i.e., it can now output a top 2 set. We refer the reader to [54] for more theoretical results on the interplay between the community structure and complex contagions.…”

Section: The Cds Methodsmentioning

confidence: 99%

Unifying information propagation models on networks and influence maximization

Tian

Lambiotte

2022

Phys. Rev. E

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Information propagation on networks is a central theme in social, behavioral, and economic sciences, with important theoretical and practical implications, such as the influence maximization problem for viral marketing.Here we consider a model that unifies the classical independent cascade models and the linear threshold models, and generalize them by considering continuous variables and allowing feedback in the dynamics. We then formulate its influence maximization as a mixed integer nonlinear programming problem and adopt derivative-free methods. Furthermore, we show that the problem can be exactly solved in the special case of linear dynamics, where the selection criterion is closely related to the Katz centrality, and propose a customized direct search method with local convergence. We then demonstrate the close to optimal performance of the customized direct search numerically on both synthetic and real networks.

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Section: The Cds Methodsmentioning

confidence: 99%

Unifying information propagation models on networks and influence maximization

Tian

Lambiotte

2022

Phys. Rev. E

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“…Finally, we remark that there do exist InfMax algorithms that are not based on greedy (Bharathi et al, 2007;Goldberg & Liu, 2013;Angell & Schoenebeck, 2017;Schoenebeck & Tao, 2017, 2019aSchoenebeck et al, 2019), but they are typically for non-submodular diffusion models.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization

Chen

Peng

Schoenebeck

et al. 2022

jair

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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1 − 1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1−1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1 − 1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular diffusion model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.

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“…This is clearly a weak assumption. For example, it is well known that even in the case of simple contagion models like the independent cascade model, the best strategy is not choosing highly influential nodes in the same closely connected neighborhood [18], but choosing sufficiently distant nodes [19]. Two main ways of alleviating this issue are considered in the literature.…”

Section: Introductionmentioning

confidence: 99%

Influence Maximization: Divide and Conquer

Patwardhan¹,

Radicchi²,

Fortunato³

2022

Preprint

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The problem of influence maximization, i.e., finding the set of nodes having maximal influence on a network, is of great importance for several applications. In the past two decades, many heuristic metrics to spot influencers have been proposed. Here, we introduce a framework to boost the performance of any such metric. The framework consists in dividing the network into sectors of influence, and then selecting the most influential nodes within these sectors. We explore three different methodologies to find sectors in a network: graph partitioning, graph hyperbolic embedding, and community structure. The framework is validated with a systematic analysis of real and synthetic networks. We show that the gain in performance generated by dividing a network into sectors before selecting the influential spreaders increases as the modularity and heterogeneity of the network increase. Also, we show that the division of the network into sectors can be efficiently performed in a time that scales linearly with the network size, thus making the framework applicable to large-scale influence maximization problems.

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Think globally, act locally: On the optimal seeding for nonsubmodular influence maximization

Cited by 4 publications

References 46 publications

Unifying information propagation models on networks and influence maximization

Unifying information propagation models on networks and influence maximization

Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization

Influence Maximization: Divide and Conquer

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