Time Constrained Influence Maximization in Social Networks

Liu, Bo; Cong, Gao; Xu, Dong; Zeng, Yanjun

doi:10.1109/icdm.2012.158

Cited by 154 publications

(148 citation statements)

References 15 publications

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“…Given a deadline T, the aim of these works is to find a set of nodes that maximizes the expected number of influenced nodes until T. In [27] a meeting event is added to diffusion models that determine the probability of meeting the neighbors in a specified time. Their proposed model is a special case of the model proposed in [28,29]. However, all of the above works ignore the devaluation of propagated items during time and overlook its effect on the influence maximization problem.…”

Section: Related Workmentioning

confidence: 99%

“…Chen et al [27] and Liu et al [28,29] independently studied the problem of influence maximization under a deadline constraint. Given a deadline T, the aim of these works is to find a set of nodes that maximizes the expected number of influenced nodes until T. In [27] a meeting event is added to diffusion models that determine the probability of meeting the neighbors in a specified time.…”

Section: Related Workmentioning

confidence: 99%

“…In [24][25][26] the underlying graph is inferred from real propagation data by considering a time delay factor. Chen et al [27] and Liu et al [28,29] independently studied the influence maximization problem with a deadline constraint. In all of the previous works, the aim is to maximize the number of influenced nodes, either until a deadline is reached or without using any time constraint.…”

Section: Introductionmentioning

confidence: 99%

“…However, in most studies, the role of time in influence propagation is ignored. Only some recent works have considered the temporal aspects in information diffusion [24][25][26][27][28][29]. In [24][25][26] the underlying graph is inferred from real propagation data by considering a time delay factor.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Time-sensitive influence maximization in social networks

Mohammadi

Saraee

Mirzaei

2015

Journal of Information Science

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One of the fundamental issues in social networks is the influence maximization problem, where the goal is to identify a small subset of individuals such that they can trigger the largest number of members in the network. In real-world social networks, the propagation of information from a node to another may incur a certain amount of time delay; moreover, the value of information may decrease over time. So not only the coverage size, but also the propagation speed matters. In this paper, we propose the Time-Sensitive Influence Maximization (TSIM) problem, which takes into account the time dependence of the information value. Considering the time delay aspect, we develop two diffusion models, namely the Delayed Independent Cascade model and the Delayed Linear Threshold model. We show that the TSIM problem is NP-hard under these models but the spread function is monotone and submodular. Thus, a greedy approximation algorithm can achieve a 1-1/e approximation ratio. Moreover, we propose two time-sensitive centrality measures and compare their performance to the greedy algorithm. We evaluate our methods on four real-world datasets. Experimental results show that the proposed algorithms outperform existing methods, which ignore the decay of information value over time.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-sensitive influence maximization in social networks

Mohammadi

Saraee

Mirzaei

2015

Journal of Information Science

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“…The task of maximizing the spread of information in a social network is a well studied problem with many works investigating different aspects of the problem [9,15,10,20]. More recently, the problem of limiting the spread of rumours or misinformation in a social network has been studied by [7,2,8].…”

Section: Related Workmentioning

confidence: 99%

Clearing Contamination in Large Networks

Simpson

Srinivasan

Thomo

2016

IEEE Trans. Knowl. Data Eng.

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In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions and Twitter. The experiments reveal that in most cases our algorithm performs near optimally.

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Influence Diffusion in Social Networks

Fan

et al. 2014

Optimization in Science and Engineering

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Time Constrained Influence Maximization in Social Networks

Cited by 154 publications

References 15 publications

Time-sensitive influence maximization in social networks

Time-sensitive influence maximization in social networks

Clearing Contamination in Large Networks

Influence Diffusion in Social Networks

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