Efficient influence maximization in social networks

Chen, Wei; Wang, Yajun; Yang, Siyu

doi:10.1145/1557019.1557047

Cited by 1,831 publications

(1,487 citation statements)

References 9 publications

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“…The only differences are the number of products and the advertising budget which are equal to 10 and 50, respectively. We benchmarked our optimization methods against two state of the art influence maximization methods, Prefix-excluding Maximum Influence Arborescence (PMIA) [25] and DegreeDiscount [9], in addition to the centrality measures.…”

Section: Real-world Datasetsmentioning

confidence: 99%

“…To our knowledge, the PMIA algorithm is the best scalable solution to the influence maximization problem under the Independent Cascade Model. -DegreeDiscount: This heuristic algorithm presented by Chen et al [9], refined the degree method by discounting the degree of nodes whenever a neighbor has already been selected as an influential node.…”

Section: Real-world Datasetsmentioning

confidence: 99%

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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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Section: Real-world Datasetsmentioning

confidence: 99%

Section: Real-world Datasetsmentioning

confidence: 99%

Scaling Influence Maximization with Network Abstractions

Maghami

Sukthankar

2014

Lecture Notes in Social Networks

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“…But these solutions are mostlybuilt for static water network. Besides sensor placement, submodularity has also been widely adopted in finding influencers [26], influence maximization [27] and network structural learning [28]. All the solutions mentioned above are not designed for incremental sensor placement, which is a more realistic problem in real-world.…”

Section: Related Workmentioning

confidence: 99%

Incremental Sensor Placement Optimization on Water Network

Huang

et al. 2013

Advanced Information Systems Engineering

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Abstract. Sensor placement on water networks is critical for the detection of accidental or intentional contamination event. With the development and expansion of cities, the public water distribution systems in cities are continuously growing. As a result, the current sensor placement will lose its effectiveness in detecting contamination event. Hence, in many real applications, we need to solve the incremental sensor placement (ISP) problem. We expect to find a sensor placement solution that reuses existing sensor deployments as much as possible to reduce cost, while ensuring the effectiveness of contamination detection. In this paper, we propose scenario-cover model to formalize ISP and prove that ISP is NPhard and propose our greedy approaches with provable quality bound. Extensive experiments show the effectiveness, robustness and efficiency of the proposed solutions.

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“…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%

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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Efficient influence maximization in social networks

Cited by 1,831 publications

References 9 publications

Scaling Influence Maximization with Network Abstractions

Scaling Influence Maximization with Network Abstractions

Incremental Sensor Placement Optimization on Water Network

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

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