Online topic-aware influence maximization

Chen, Shuo; Fan, Jiulun; Li, Guoliang; Feng, Jianhua; Tan, Kian-Lee; Tang, Jinhui

doi:10.14778/2735703.2735706

Cited by 202 publications

(135 citation statements)

References 23 publications

(39 reference statements)

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“…The problem of identifying influentials has gained a lot of attention from several research communities as it has applications in viral marketing [12], [13], [14], disease prevention [15] and propagation [16], politics [17], [18], etc.…”

Section: A Identification Of Influential Usersmentioning

confidence: 99%

Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Eirinaki

Moniz

Potika

2016

2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sus

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Abstract-The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process. Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading occurs in several rounds and has fixed number of influentials. In our approach we consider only one round of influence, which finds applications in settings where timely influence is vital. We tackle the problem by proposing a two-phase framework that aims at identifying influentials in the first phase and form influential neighborhoods to generate recommendations to users with no prior knowledge in the second phase. The difference of the proposed framework with most social recommender systems is that we need to generate recommendations including more than one item and in the absence of explicit ratings, solely relying on the social network's graph.

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Section: A Identification Of Influential Usersmentioning

confidence: 99%

Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Eirinaki

Moniz

Potika

2016

2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sus

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“…(1) Topic-Aware and Competitive IM: The motive of topicaware IM [8,11,15] is to design strategies for maximizing the collective spread of information under a given topic-distribution. It allows different influence probabilities for each topic (assuming a joint distribution of topics), without considering the opinions (of a node) towards these topics.…”

Section: Related Workmentioning

confidence: 99%

Holistic Influence Maximization

Galhotra¹,

Arora²,

Roy³

2016

Proceedings of the 2016 International Conference on Management of Data

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The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIMthe opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspectmemory consumption and running time, of the IM problem as well.Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets. Categories and Subject Descriptors MOTIVATIONGrowth and pervasiveness of online social networks is no longer a new phenomenon. They have become an integral part of the dayto-day life of almost every Internet user. Their wide-spread reach * The first two authors have contributed equally to this work.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. with its applicability in solving the above mentioned problems and beyond, has thus, been one of the most widely studied problems over the past decade. This problem is to identify a set of seed nodes so that the overall spread of information in a network, which is the potential collective impact of imparting that piece of information to these nodes, is maximized. Given that the objective of IM is to maximize the spread of information about a content, which can be a product, person, event and many more, an important aspect of this problem is the underlying information diffusion model. A diffusion model defines the dynamics of information propagation and also controls the way this information is perceived by the nodes in a network. Nevertheless, a substantially large fraction of the literature in this field has focussed on devising efficient and scalable algorithms [13, 17-19, 21-23, 28, 29, 31-33, 35, 42, 46] for IM using the classical information diffusion models [32,44]. The two fundamental diffusion models p...

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“…In none of the works mentioned above, context information is considered. IM based on context information is studied in several other works such as [8], [10], [11]. However, these works assume that the influence probabilities are known and topics/contexts are discrete.…”

Section: A Influence Maximizationmentioning

confidence: 99%

“…Hence, the characteristics (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 [8]- [11]. 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%

Online Contextual Influence Maximization in social networks

Omer

Karakurt

Tekin

2016

2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-In this paper, we propose the Online Contextual Influence Maximization Problem (OCIMP). In OCIMP, the learner faces a series of epochs in each of which a different influence campaign is run to promote a certain product in a given social network. In each epoch, the learner first distributes a limited number of free-samples of the product among a set of seed nodes in the social network. Then, the influence spread process takes place over the network, other users get influenced and purchase the product. The goal of the learner is to maximize the expected total number of influenced users over all epochs. We depart from the prior work in two 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. We develop a learning algorithm for OCIMP, called Contextual Online INfluence maximization (COIN). COIN can use any approximation algorithm that solves the offline influence maximization problem 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 COIN achieves sublinear regret with respect to an approximation oracle that knows the influence probabilities for all contexts. Moreover, our regret bound holds for any sequence of contexts. We also test the performance of COIN on several social networks, and show that it performs better than other methods.

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Online topic-aware influence maximization

Cited by 202 publications

References 23 publications

Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Holistic Influence Maximization

Online Contextual Influence Maximization in social networks

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