Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions

Badanidiyuru, Ashwinkumar; Papadimitriou, Christos H.; Rubinstein, Aviad; Seeman, Lior; Singer, Yoram

doi:10.1137/1.9781611974331.ch31

Cited by 45 publications

(39 citation statements)

References 45 publications

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“…* Corresponding author: Jing Tang. problem asks for a set of seed users S from network G at the cost of c(S) so as to maximize the total expected profit, i.e., the expected spread of S less the total investment cost c(S). However, this vanilla PM problem overlooks one fact that even though the advertisers have the full knowledge about the whole network, they are likely to only have access to a fraction of users [4], which is quite common in marketing applications. For example, companies advertise new products to users in their subscription mailing list, or new shop owners provide free samples to the popularities or celebrities who visit their store on site, to name a few.…”

Section: Introductionmentioning

confidence: 99%

Efficient algorithms for adaptive influence maximization

et al. 2018

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Given a social network G, the profit maximization (PM) problem asks for a set of seed nodes to maximize the profit, i.e., revenue of influence spread less the cost of seed selection. The target profit maximization (TPM) problem, which generalizes the PM problem, aims to select a subset of seed nodes from a target user set T to maximize the profit. Existing algorithms for PM mostly consider the nonadaptive setting, where all seed nodes are selected in one batch without any knowledge on how they may influence other users. In this paper, we study TPM in adaptive setting, where the seed users are selected through multiple batches, such that the selection of a batch exploits the knowledge of actual influence in the previous batches. To acquire an overall understanding, we study the adaptive TPM problem under both the oracle model and the noise model, and propose ADG and ADDATP algorithms to address them with strong theoretical guarantees, respectively. In addition, to better handle the sampling errors under the noise model, we propose the idea of hybrid error based on which we design a novel algorithm HATP that boosts the efficiency of ADDATP significantly. We conduct extensive experiments on real social networks to evaluate the performance, and the experimental results strongly confirm the superiorities and effectiveness of our solutions.

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Section: Introductionmentioning

confidence: 99%

Efficient algorithms for adaptive influence maximization

et al. 2018

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“…Recently, Seeman and Singer initiated a line of works [Seeman and Singer 2013;Badanidiyuru et al 2014;Rubinstein et al 2015] on adaptive seeding in social networks that is closely related to ours. In the adaptive seeding problem, a small subset X of the nodes in a social network is initially available to an advertiser.…”

Section: Related Workmentioning

confidence: 80%

Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization

Chen

Lin

et al. 2015

Proceedings of the Sixteenth ACM Conference on Economics and Computation

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In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation.We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of (1 − 1/e − ε) 3 for any (polynomially small) ε > 0. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.

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“…Correa et al [39] show that the adaptivity benefit is bounded if every pair of nodes randomly meet at the same rate. Badanidiyuru et al [40] propose an algorithm based on locally-adaptive policies.…”

Section: Related Workmentioning

confidence: 99%

A two phase investment game for competitive opinion dynamics in social networks

Dhamal

Ben‐Ameur

Chahed

et al. 2020

Information Processing & Management

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We propose a setting for two-phase opinion dynamics in social networks, where a node's final opinion in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions, by strategically investing on nodes in the two phases. A node's initial opinion in the second phase naturally plays a key role in determining the final opinion of that node, and hence also of other nodes in the network due to its influence on them. However, more importantly, this bias also determines the effectiveness of a camp's investment on that node in the second phase. In order to formalize this two-phase investment setting, we propose an extension of Friedkin-Johnsen model, and hence formulate the utility functions of the camps. We arrive at a decision parameter which can be interpreted as two-phase Katz centrality. There is a natural tradeoff while splitting the available budget between the two phases. A lower investment in the first phase results in worse initial biases in the network for the second phase. On the other hand, a higher investment in the first phase spares a lower available budget for the second phase, resulting in an inability to fully harness the influenced biases. We first analyze the non-competitive case where only one camp invests, $ A previous, preliminary, concise version of this paper was presented at The Joint International Workshop on Social Influence Analysis and Mining Actionable Insights from Social Networks (workshop with IJCAI-ECAI), Stockholm, Sweden, 2018 [1].for which we present a polynomial time algorithm for determining an optimal way to split the camp's budget between the two phases. We then analyze the case of competing camps, where we show the existence of Nash equilibrium and that it can be computed in polynomial time under reasonable assumptions. We conclude our study with simulations on real-world network datasets, in order to quantify the effects of the initial biases and the weightage attributed by nodes to their initial biases, as well as that of a camp deviating from its equilibrium strategy. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to effectively influence the biases to be harnessed in the second phase.

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Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions

Cited by 45 publications

References 45 publications

Efficient algorithms for adaptive influence maximization

Efficient algorithms for adaptive influence maximization

Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization

A two phase investment game for competitive opinion dynamics in social networks

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