Maximizing the Influence and Profit in Social Networks

Zhu, Yuqing; Li, Deying; Yan, Ruidong; Wu, Weili; Bi, Yuanjun

doi:10.1109/tcss.2017.2715183

Cited by 37 publications

(14 citation statements)

References 18 publications

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“…Specifically, market strategy makers seek for a proper set of influential individuals and invest in each of them (e.g., cashback rewards, coupons, or discounts) to exert their influence on advertising, aiming to maximize the expected profit. This problem is commonly studied as the profit maximization (PM) problem in the literature [2], [19], [23], [26], [27], [32]. PM A short version of the paper will appear in the 36th IEEE International Conference on Data Engineering (ICDE '20), April 20-24, 2020, Dallas, Texas, USA.…”

Section: Introductionmentioning

confidence: 99%

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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%

“…Existing work on profit maximization mostly focuses on the nonadaptive setting [2], [19], [23], [26], [27], [32], where all seed nodes are selected in one batch without any knowledge on how they may influence other users. As a consequence of the nonadaptiveness, the maximal possible profit might not be achieved.…”

Section: Introductionmentioning

confidence: 99%

Efficient algorithms for adaptive influence maximization

et al. 2018

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“…More models of multiple influences competing in a network are considered by researchers [9][10][11][12][13][14]. Furthermore, sale strategies for virus marketing are also studied [15]. Most previous works focused on the issue of influence propagation within a single network in the past decade.…”

Section: Influence Maximization In a Single Networkmentioning

confidence: 99%

Maximizing multiple influences and fair seed allocation on multilayer social networks

et al. 2020

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The dissemination of information on networks involves many important practical issues, such as the spread and containment of rumors in social networks, the spread of infectious diseases among the population, commercial propaganda and promotion, the expansion of political influence and so on. One of the most important problems is the influence-maximization problem which is to find out k most influential nodes under a certain propagate mechanism. Since the problem was proposed in 2001, many works have focused on maximizing the influence in a single network. It is a NP-hard problem and the state-of-art algorithm IMM proposed by Youze Tang et al. achieves a ratio of 63.2% of the optimum with nearly linear time complexity. In recent years, there have been some works of maximizing influence on multilayer networks, either in the situation of single or multiple influences. But most of them study seed selection strategies to maximize their own influence from the perspective of participants. In fact, the problem from the perspective of network owners is also worthy of attention. Since network participants have not had access to all information of the network for reasons such as privacy protection and corporate interests, they may have access to only part of the social network. The owners of networks can get the whole picture of the networks, and they need not only to maximize the overall influence, but also to consider allocating seeds to their customers fairly, i.e., the Fair Seed Allocation (FSA) problem. As far as we know, FSA problem has been studied on a single network, but not on multilayer networks yet. From the perspective of network owners, we propose a multiple-influence diffusion model MMIC on multilayer networks and its FSA problem. Two solutions of FSA problem are given in this paper, and we prove theoretically that our seed allocation schemes are greedy. Subsequent experiments also validate the effectiveness of our approaches.

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“…The TPM problem degenerates to the PM problem if the target set T contains all users in the social network, i.e., T = V . Existing work on profit maximization mostly focuses on the non-adaptive setting [6,7,25,26], where all seed nodes are selected in one batch without any knowledge on how they may influence other users. As a consequence of the non-adaptiveness, the maximal possible profit might not be achieved.…”

Section: Problems and Motivationsmentioning

confidence: 99%

“…the definition ofΓ φ (S), with probability p(x),Γ φ (S) = 0; and with probability 1 − p(x),Γ φ (S) = η. As a consequence, we haveE[Γ φ (S)] = η 1 − E[p(x)] ,(A 25). where the expectation on the right hand side is taken with respect to the randomness of k. Let f (x) be the ratio of E[Γ φ (S)] to Γ φ (S), which is given byf (x) := E[Γ φ (S)] Γ φ (S) = η 1 − E[p(x)] min{x, η} .…”

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confidence: 99%

Efficient adaptive approximation algorithms in online social networks

Huang¹

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Online social networks (OSNs) have witnessed their prosperous developments in recent years. Based on the established relations among individuals in OSNs, ideas and opinions can be spread via a word-of-mouth effect. To exploit this effect, many companies have taken OSNs as the major advertising channels to promote their products, which has motivated substantial research on viral marketing. Among them, influence maximization, seed minimization, and profit maximization have been extensively studied in the past decades. These three problems are mainly studied in the non-adaptive setting in the literature, i.e., seed user selection phase does not involve any observations on influence propagation in reality. In this thesis, we extend profit maximization into a generalized version as target profit maximization and investigate these three problems in the adaptive setting, formulated as adaptive influence maximization, adaptive seed minimization, and adaptive target profit maximization respectively. Firstly, we study the adaptive influence maximization (IM) problem. Given a social network G and an integer k, the influence maximization problem asks for a seed set S of k nodes from G to maximize the expected number of nodes activated via an influence propagation. In the adaptive setting, the k seed nodes are selected in batches of equal size b, such that the i-th batch is identified after the actual influence results of the former i − 1 batches are observed. We propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of 1 − e α(ε−1) with high probability, where ε ∈ (0, 1) is a user-specified parameter and α = 1−(1−1/b) b is set by the batch size b. Our approach is based on a novel general framework AdaptGreedy which could be instantiated by any existing non-adaptive IM algorithms with expected approximation guarantee. In this regard, we design a novel non-adaptive IM algorithm called EPIC which not only provides strong expected approximation guarantee, but also i First and foremost, I would like to express my sincerest gratitude towards my supervisors, Prof. Xiaokui Xiao and Prof. Aixin Sun. It is their guidance of patience on my PhD research that makes the accomplishment of this thesis possible. In particular, Prof. Xiao's rigorous attitude, the pursuit of high standards, and unlimited enthusiasm for research have impressed and shaped me deeply. Prof. Sun, who comes from the hometown of Confucius in China, well interprets the charms of being a professor. It is his continuous help and care that support me forward. Their positive influence on me would be the priceless treasure for my future career and life. Then, I would like to thank my collaborators, especially Dr. Jing Tang. We have collaborated for two years. I learn so much from his great intelligence and experience on research such that I could conquer the challenges in my research.

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Maximizing the Influence and Profit in Social Networks

Cited by 37 publications

References 18 publications

Efficient algorithms for adaptive influence maximization

Efficient algorithms for adaptive influence maximization

Maximizing multiple influences and fair seed allocation on multilayer social networks

Efficient adaptive approximation algorithms in online social networks

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