Evolutionary Information Diffusion over Heterogeneous Social Networks

Cao, Xuanyu; Chen, Yan; Jiang, Chunxiao; Liu, K. J. Ray

doi:10.1109/tsipn.2016.2613680

Cited by 30 publications

(19 citation statements)

References 34 publications

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“…Similarly, in viral marketing, a node gets activated when it purchases the marketed product. Similar to the previous setting, the goal of the learner is to minimize expectation of the regret given in (2). For this purpose, we propose a variant of COIN-CO-EL called COIN-CO-NL, which is able to achieve sublinear regret when only costly node-level feedback is available.…”

Section: Contextual Online Influence Maximization With Costly Nodmentioning

confidence: 99%

“…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%

“…The learner does not have access to a i,j 's beforehand, but can observe them by paying a cost c for each observation. 2 COIN-CO-EL keeps two counters f Q i,j (t) and s Q i,j (t) for each (i, j) ∈ E and each Q ∈ Q. The former denotes the number of observed failed activation attempts on edge (i, j) at epochs prior to epoch t when the context was in Q, while the latter denotes the number of observed successful activation attempts on edge (i, j) in epochs prior to epoch t when the context was in Q.…”

mentioning

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: Contextual Online Influence Maximization With Costly Nodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

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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“…To take user's collective behavior into consideration, the LT model assumed that a user would accept a certain piece of information when the percentage of his/her neighbors who had adopted the information was above a threshold. Recently, the evolutionary dynamics of the natural ecological systems has been introduced to model the information diffusion over the social networks (Jiang et al, 2014a(Jiang et al, , 2014bCao et al, 2016). The authors modeled the information diffusion process with evolutionary game theory over the synthetic and real networks, and the evolutionary stable states were analyzed with respect to different types of information and different network structures.…”

Section: Introductionmentioning

confidence: 99%

An epidemic model for correlated information diffusion in crowd intelligence networks

Zhao

Chen

2019

IJCS

Self Cite

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Purpose With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks. Design/methodology/approach This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them. Findings The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model. Originality/value The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

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“…A huge amount of information will be created at each moment and we have entered into the big data era [1][2][3]. Meanwhile, it is the rich availability of these information that changes the paradigm of scientific researches and accelerates the data-driven efforts within the studies of many problems, such as behaviorial decision making [4,5], population mobility [6,7], information and rumor diffusion [8][9][10], product marketing strategy [11,12], epidemic spreading [13][14][15], virus or malware propagation [16,17], and even social movements and political campaigns [18,19], to name some examples. On the one hand, making the information much more available is beneficial to make the rational choices during the everyday life, studies, career and research, even help to implement the scientific discoveries across many disciplines.…”

Section: Introductionmentioning

confidence: 99%

Doubly effects of information sharing on interdependent network reciprocity

et al. 2018

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Understanding large-scale cooperation among unrelated individuals is one of the greatest challenges of the 21st century. Since human cooperation evolves on social networks, the theoretical framework of multilayer networks is perfectly suited for studying this fascinating aspect of our biology. To that effect, we here study the cooperation in evolutionary games on interdependent networks, such that players in one network layer play the snowdrift game (SDG), and the prisoner's dilemma game (PDG) in the other layer. Importantly, players are able to share information across two layers, which in turn affects their strategy choices. Monte Carlo simulations reveal that the transfer of information about the strategy of the corresponding player in the other network layer alone is enough to significantly promote the overall level of cooperation. However, while the cooperation is markedly enhanced in the layer where the PDG is played, the opposite is true, albeit to a lesser extent, for the layer where the SDG is played. The net increase in cooperation is thus due to a doubly effect of information sharing. We show further that the more complete the information transfer, the more the overall level of cooperation is promoted, and that this holds as long as the information channels between the player do not vary over time. We discuss potential implications of these findings for future human experiments concerning the cooperation on multilayer networks.

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Evolutionary Information Diffusion over Heterogeneous Social Networks

Cited by 30 publications

References 34 publications

Online Contextual Influence Maximization With Costly Observations

Online Contextual Influence Maximization With Costly Observations

An epidemic model for correlated information diffusion in crowd intelligence networks

Doubly effects of information sharing on interdependent network reciprocity

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