Influence maximization in social networks under Deterministic Linear Threshold Model

Gursoy, Furkan; Günneç, Dilek

doi:10.1016/j.knosys.2018.07.040

Cited by 39 publications

(10 citation statements)

References 13 publications

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“…For example, the objective function is no longer submodular, a key property to obtain the theoretical approximation guarantee for various greedy algorithms. However, properties like submodularity are arguably strict, and breaking the boundary of such properties is necessary for the IM problem to embrace a wider family of diffusion models in practice [22,29]. Therefore, we propose a general framework for the IM problem, based on MINLP and derivative-free methods, where only evaluations of the objective function is required.…”

Section: Discussionmentioning

confidence: 99%

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Unifying information propagation models on networks and influence maximisation

Tian,

Lambiotte

2021

Preprint

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Information diffusion in social networks is a central theme in computational social sciences, with important theoretical and practical implications, such as the influence maximisation problem for viral marketing. Two widely adopted diffusion models are the independent cascade model where nodes adopt their behaviour from each neighbour independently, and the linear threshold model where collective effort from the whole neighbourhood is needed to influence a node. However, both models suffer from certain drawbacks, including a binary state space, where nodes are either active or not, and the absence of feedback, as nodes can not be influenced after having been activated. To address these issues, we consider a model with continuous variables that has the additional advantage of unifying the two classic models, as the extended independent cascade model and the extended linear threshold model are recovered by setting appropriate parameters. For the associated influence maximisation problem, the objective function is no longer submodular, a feature that most approximation algorithms are based on but is arguably strict in practice. Hence, we develop a framework, where we formulate the influence maximisation problem as a mixed integer nonlinear programming and adopt derivative-free methods. Furthermore, we propose a customised direct search method specifically for the proposed diffusion model, with local convergence. We also show that the problem can be exactly solved in the case of linear dynamics by selecting nodes according to their Katz centrality. We demonstrate the rich behaviour of the newly proposed diffusion model and the close-to-optimal performance of the customised direct search numerically on both synthetic and real networks.

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Section: Discussionmentioning

confidence: 99%

“…By theorem 3.2, the general diffusion model reaches the linear-dynamics extreme. Then as in (22), the objective function is linear in its continuous variables,…”

Section: Special Casesmentioning

confidence: 99%

Unifying information propagation models on networks and influence maximisation

Tian,

Lambiotte

2021

Preprint

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“…Classic IC model treats the diffusion activity of information as cascades while the LT model determines infections of users according to thresholds of the influence pressure incoming from the neighborhood. Both of them can be unified into a same framework [48], and a series of extension work has been proposed [6,50,51,7,52,53,54]. For example, [50] extends the IC model to formulate a generative model that can take time delay into consideration.…”

Section: Information Cascade Predictionmentioning

confidence: 99%

Deep Collaborative Embedding for information cascade prediction

Zhao

Yang

Lin

et al. 2020

Knowledge-Based Systems

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Recently, information cascade prediction has attracted increasing interest from researchers, but it is far from being well solved partly due to the three defects of the existing works. First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion. Second, the existing works often ignore the prediction of the infection order, which also plays an important role in social network analysis. At last, the existing works often depend on the requirement of underlying diffusion networks which are likely unobservable in practice. In this paper, we aim at the prediction of both node infection and infection order without requirement of the knowledge about the underlying diffusion mechanism and the diffusion network, where the challenges are two-fold. The first is what cascading characteristics of nodes should be captured and how to capture them, and the second is that how to model the non-linear features of nodes in information cascades. To address these challenges, we propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction, which can capture not only the node structural property but also two kinds of node cascading characteristics. We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration, in which way the non-linearity of information cascades * Ning Yang and Tao Lin share the corresponding authorship.can be effectively captured. The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.

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“…But Kempe et al [7] showed that solving an influence maximization problem is NP-hard. As a result of this, most of the existing models in literature are based on extensions of either Independent Cascade (IC) model [13] or Linear Threshold (LT) model [6]. However, these models and their extensions rely on assumed probabilistic values to represent influence [9,11].…”

Section: Introductionmentioning

confidence: 99%

Influence maximization through user interaction modeling

Oriedi

Runz

Guessoum

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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A majority of influence maximization models in social networks in literature are based on a seminal work by Kempe et al., in which two classic influence models were proposed i.e Linear Threshold Model and Independent Cascade Model. However, these two models use assumed values to model influence and influence propagation in social networks. This may lead to inaccurate approximation of influence. In this work, we model influence from actual social actions among members of a social network through a proposed algorithm -Selective Breadth First Traversal -that efficiently generates an optimal seed set for influence maximization. Experimental results on real data show that our approach provides an improvement over a number of traditional influence maximization algorithms.

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Influence maximization in social networks under Deterministic Linear Threshold Model

Cited by 39 publications

References 13 publications

Unifying information propagation models on networks and influence maximisation

Unifying information propagation models on networks and influence maximisation

Deep Collaborative Embedding for information cascade prediction

Influence maximization through user interaction modeling

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