Real-Time Topic-Aware Influence Maximization Using Preprocessing

Chen, Wei; Lin, Tzu-Jung; Yang, Cheng

doi:10.1007/978-3-319-21786-4_1

Cited by 21 publications

(27 citation statements)

References 24 publications

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“…None of the works mentioned above consider the context information. IM based on context information is studied in several other works such as [4], [6], [7]. However, in contrast to our work which solves a more general problem, these works assume that the influence probabilities are known and topics/contexts are discrete.…”

Section: A Influence Maximizationmentioning

confidence: 96%

“…In recent years, there has been growing interest in understanding how influence spreads in a social network [2], [3], [4]- [7]. This interest is motivated by the proliferation of viral marketing in social networks.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the characteristic (context) of the product affects the influence probabilities. The strand of literature that is closest to the problem we consider in this paper in terms of the dependence of the influence probabilities on the context is called topic-aware IM [4]- [7]. To the best of our knowledge, none of the prior works in topic-aware IM develop learning algorithms with provable performance guarantees for the case when the influence probabilities are unknown.…”

Section: Introductionmentioning

confidence: 99%

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Online Contextual Influence Maximization With Costly Observations

Saritac

Karakurt

Tekin

2019

IEEE Trans. on Signal and Inf. Process. over Networks

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In the Online Contextual Influence Maximization Problem with Costly Observations, the learner faces a series of epochs in each of which a different influence spread process takes place over a network. At the beginning of each epoch, the learner exogenously influences (activates) a set of seed nodes in the network. Then, the influence spread process takes place over the network, through which other nodes get influenced. The learner has the option to observe the spread of influence by paying an observation cost. The goal of the learner is to maximize its cumulative reward, which is defined as the expected total number of influenced nodes over all epochs minus the observation costs. We depart from the prior work in three aspects: (i) the learner does not know how the influence spreads over the network, i.e., it is unaware of the influence probabilities; (ii) influence probabilities depend on the context; (iii) observing influence is costly. We consider two different influence observation settings: costly edge-level feedback, in which the learner freely observes the set of influenced nodes, but pays to observe the influence outcomes on the edges of the network; and costly node-level feedback, in which the learner pays to observe whether a node is influenced or not. Since the offline influence maximization problem itself is NP-hard, for these settings, we develop online learning algorithms that use an approximation algorithm as a subroutine to obtain the set of seed nodes in each epoch. When the influence probabilities are Hölder continuous functions of the context, we prove that these algorithms achieve sublinear regret (for any sequence of contexts) with respect to an approximation oracle that knows the influence probabilities for all contexts. Our numerical results on several networks illustrate that the proposed algorithms perform on par with the state-of-the-art methods even when the observations are cost-free. .tr.A preliminary version of this work has appeared in Allerton 2016 [1].

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Section: A Influence Maximizationmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Contextual Influence Maximization With Costly Observations

Saritac

Karakurt

Tekin

2019

IEEE Trans. on Signal and Inf. Process. over Networks

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“…In [38], the authors pointed out that most of influence maximization research only utilize an individual's ability to influence another but ignores individuals' conformity which is a person's inclination to be influenced. Two models C 2 and C 3 are proposed to support their observation.…”

Section: Heterogeneous Modelmentioning

confidence: 99%

Influence analysis: A survey of the state-of-the-art

Han¹,

Li²

2018

Mathematical Foundations of Computing

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Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference. 201 202 MENG HAN AND YINGSHU LIachieved. Even so, prior to the Internet, quantitative data of social networks were scanty and the further influence analysis in social networks was in the slow-lane. In 2007, Nicholas et al. [47] published their years of research results based on the historical data from the spreading of obesity over 32 years. From the same research filed, David et al. [11] proposed another idea about the spread of obesity in social networks based on the simulations which further considered the group effect in obesity spreading. Both works tried to explore how the influence diffusion in social networks affects obesity. In their model, individuals' influence over each other rely on food intake and physical activities [70]. Since other models consider obesity as a "contagious" phenomenon that can be caught if most social contracts are deemed obese, the interaction of social networks with environmental factors could not be explored. It was not accounted for in the general model where the social networks were proposed as a means to mitigate the obesity epidemic. Many other research results have been obtained recently, such as smoking behavior [48][52], happiness [68], and loneliness [25][117] which spread along a social network over time.At an American high school, Salath et al.[207] obtained high-resolution data of close proximity interactions during a typical day, and their work helps with the reconstruction of a social network for infectious disease transmission by using wireless sensor network technology [246,98]. Through simulations, they showed that targeted immunization using the contact-network data is much more effective than random immunization. Stehle et al. [216] report a similar result like Salath [207] in a French primary school. The team headed by Stehle also provided several public-health implications of infectious diseases by collecting a period of history data from their experiments. By analyzing the real experiments in two ...

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“…By utilizing the MIA model to approximate the computation of influence spread, Chen et al () proposed a best‐first framework with

1 - 1 / e

approximation ratio and devised efficient algorithms to address the problem. Chen, Lin, and Yang () proposed to utilize the preprocessing methods for topics to avoid redoing influence maximization for each item from scratch. Chu et al () proposed effective techniques to estimate the influence bound and utilize the bound to do pruning.…”

Section: Related Workmentioning

confidence: 99%

Location‐aware targeted influence maximization in social networks

Cheng

et al. 2017

Asso for Info Science & Tech

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In this paper, we study the location‐aware targeted influence maximization problem in social networks, which finds a seed set to maximize the influence spread over the targeted users. In particular, we consider those users who have both topic and geographical preferences on promotion products as targeted users. To efficiently solve this problem, one challenge is how to find the targeted users and compute their preferences efficiently for given requests. To address this challenge, we devise a TR‐tree index structure, where each tree node stores users' topic and geographical preferences. By traversing the TR‐tree in depth‐first order, we can efficiently find the targeted users. Another challenge of the problem is to devise algorithms for efficient seeds selection. We solve this challenge from two complementary directions. In one direction, we adopt the maximum influence arborescence (MIA) model to approximate the influence spread, and propose two efficient approximation algorithms with 1−1/e approximation ratio, which prune some candidate seeds with small influences by precomputing users' initial influences offline and estimating the upper bound of their marginal influences online. In the other direction, we propose a fast heuristic algorithm to improve efficiency. Experiments conducted on real‐world data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

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Real-Time Topic-Aware Influence Maximization Using Preprocessing

Cited by 21 publications

References 24 publications

Online Contextual Influence Maximization With Costly Observations

Online Contextual Influence Maximization With Costly Observations

Influence analysis: A survey of the state-of-the-art

Location‐aware targeted influence maximization in social networks

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