Scalable influence maximization for prevalent viral marketing in large-scale social networks

Chen, Wei; Wang, Chi; Wang, Yajun

doi:10.1145/1835804.1835934

Cited by 1,463 publications

(1,419 citation statements)

References 14 publications

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“…They do not have positive or negative weights associated with their edges. Both Independent Cascade (IC) model [1,7,13,14,28] and Linear Threshold (LT) [1] model use unsigned networks. Kempe et al [1] studied the problem of identifying the influential set of nodes in order to maximize the spread of influence.…”

Section: Unsigned Network Modelmentioning

confidence: 99%

“…The greedy algorithm proposed by Kempe et al [1] and its improvements are too slow and unscalable. Therefore, W. Chen [7] considered scalability factor and designed a new heuristic algorithm which is easily scalable to billions of nodes as well as edges in the experiments. S. Bharthi [13] studies the influence maximization problem when multiple companies are competing to promote their products or services using viral marketing.…”

Section: Unsigned Network Modelmentioning

confidence: 99%

“…Motivated by this background, the community of researchers has recently studied the aspect of influence maximization in social networks for viral marketing [1,2,3,4,5,6,7,8,9]. Influence Maximization is a fundamental data mining problem concerning the propagation of ideas, opinions, and innovations through social networks.…”

Section: Introductionmentioning

confidence: 99%

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Blocking negative influential node set in social networks: from host perspective

Kaur

2015

Trans. Emerging Tel. Tech.

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Nowadays, social networks are considered as the very important medium for the spreading of information, innovations, ideas and influences among individuals. Viral marketing is a most prominent marketing strategy using word‐of‐mouth advertising in social networks. The key problem with the viral marketing is to find the set of influential users or seeds, who, when convinced to adopt an innovation or idea, shall influence other users in the network, leading to a large number of adoptions. In this study, we study the competitive viral marketing problem from host perspective, where the host of the social network (such as Facebook, Twitter, etc.) sells the viral marketing campaigns to its customers and keeps control of the allocation of seeds. Seeds are allocated by creating ‘bang for the buck’ for each company. In this paper, we first propose a new influence diffusion model considering both negative and positive influences. Subsequently, we propose a novel optimization problem, named Blocking Negative Influential Node Set (BNINS) selection problem, to identify the positive node set (seeds) such that the number of negatively activated nodes is minimised for all competitors. Finally, we proposed a greedy algorithm called BNINS‐GREEDY to solve BNINS and conducted comprehensive experiments and simulations to validate the proposed method. The results show that, for random graphs, on average, BNINS‐GREEDY blocks the negative influence 17.22 per cent more than the most related work called Competitive Linear Directed Acyclic Graph. Moreover, on the real Epinions dataset, BNINS‐GREEDY achieves 7.6 per cent more positive influence propagation than Competitive Linear Directed Acyclic Graph. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Unsigned Network Modelmentioning

confidence: 99%

Section: Unsigned Network Modelmentioning

confidence: 99%

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Blocking negative influential node set in social networks: from host perspective

Kaur

2015

Trans. Emerging Tel. Tech.

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“…Examples of possible speedups include innovations such as the use of a shortest-path based influence cascade model [16] or a lazy-forward optimization algorithm [19] to reduce the number of evaluations on the influence spread of nodes. Clever heuristics have been used very successfully to speed computation in both the LT model (e.g., the PMIA algorithm [8]) and also the IC model [25]. In this chapter, instead of using the original cascade models by Kempe et al we introduce a cascade model that accounts for product interactions and community differences in influence propagation.…”

Section: Related Workmentioning

confidence: 99%

“…Commonly used propagation models such as LTM (Linear Threshold Model) and ICM (Independent Cascade Model) assume that a node's adoption probability is conditioned on the opinions of the local network neighborhood [15]. Much of the previous influence maximization work [10,8,25] uses these two interaction models. Since the original LT model and IC model, other generalized models have been proposed for different domains and specialized applications.…”

Section: Introductionmentioning

confidence: 99%

Scaling Influence Maximization with Network Abstractions

Maghami

Sukthankar

2014

Lecture Notes in Social Networks

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Abstract. Maximizing product adoption within a customer social network under a constrained advertising budget is an important special case of the general influence maximization problem. Specialized optimization techniques that account for product correlations and community effects can outperform network-based techniques that do not model interactions that arise from marketing multiple products to the same consumer base. However, it can be infeasible to use exact optimization methods that utilize expensive matrix operations on larger networks without parallel computation techniques. In this chapter, we present a hierarchical influence maximization approach for product marketing that constructs an abstraction hierarchy for scaling optimization techniques to larger networks. An exact solution is computed on smaller partitions of the network, and a candidate set of influential nodes is propagated upward to an abstract representation of the original network that maintains distance information. This process of abstraction, solution, and propagation is repeated until the resulting abstract network is small enough to be solved exactly.

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Influence diffusion detection using the influence style (INFUSE) model

Tan

Ding

2015

Asso for Info Science & Tech

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Blogs are readily available sources of opinions and sentiments that in turn could influence the opinions of the blog readers. Previous studies have attempted to infer influence from blog features, but they have ignored the possible influence styles that describe the different ways in which influence is exerted. We propose a novel approach to analyzing bloggers' influence styles and using the influence styles as features to improve the performance of influence diffusion detection among linked bloggers. The proposed influence style (INFUSE) model describes bloggers' influence through their engagement style, persuasion style, and persona. Methods used include similarity analysis to detect the creating−sharing aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. We further extend the INFUSE model to detect influence diffusion among linked bloggers based on the bloggers' influence styles. The INFUSE model performed well with an average F1 score of 76% compared with the in‐degree and sentiment‐value baseline approaches. Previous studies have focused on the existence of influence among linked bloggers in detecting influence diffusion, but our INFUSE model is shown to provide a fine‐grained description of the manner in which influence is diffused based on the bloggers' influence styles.

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Scalable influence maximization for prevalent viral marketing in large-scale social networks

Cited by 1,463 publications

References 14 publications

Blocking negative influential node set in social networks: from host perspective

Blocking negative influential node set in social networks: from host perspective

Scaling Influence Maximization with Network Abstractions

Influence diffusion detection using the influence style (INFUSE) model

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