Exploring influence maximization in online and offline double-layer propagation scheme

Yang, Yintang; Xu, Yuanbo; Wang, En; Lou, Kaihao; Luan, Dongming

doi:10.1016/j.ins.2018.03.048

Cited by 24 publications

(7 citation statements)

References 14 publications

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“…In [98], if the euclidean distance between two users in time t is less than a defined threshold, these users are considered as neighbors in the offline layer; this implies that edges in that layer are dynamic and change over the time. To select a seed set, the network is then compressed into a single layer and the problem is solved using a simulation-based strategy.…”

Section: Physical Relationship-aware Methodsmentioning

confidence: 99%

“…To select a seed set, the network is then compressed into a single layer and the problem is solved using a simulation-based strategy. In addition, a centralitybased method is proposed in [98] to identify a set of influential seeds with lower overlap. In [54], according to the ratio of attendance of two users in a common place over a time period, a weight is assigned to each offline edge.…”

Section: Physical Relationship-aware Methodsmentioning

confidence: 99%

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Influence Maximization in Social Networks: A Survey of Behaviour-Aware Methods

Zareie¹,

Sakellariou²

2021

Preprint

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Social networks have become an increasingly common abstraction to capture the interactions of individual users in a number of everyday activities and applications. As a result, the analysis of such networks has attracted lots of attention in the literature. Among the topics of interest, a key problem relates to identifying so-called influential users for a number of applications, such as spreading messages, viral marketing or preventing rumour propagation. Several approaches have been proposed to estimate users' influence and identify sets of influential users in social networks. A common basis of these approaches is to consider links between users, that is, structural or topological properties of the network. To a lesser extent, some approaches take into account users' behaviours or attitudes. Although a number of surveys have reviewed approaches based on structural properties of social networks, there has been no comprehensive review of approaches that take into account users' behaviour. This paper attempts to cover this gap by reviewing and proposing a taxonomy of such behaviour-aware methods to identify influential users in social networks.

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Section: Physical Relationship-aware Methodsmentioning

confidence: 99%

Section: Physical Relationship-aware Methodsmentioning

confidence: 99%

Influence Maximization in Social Networks: A Survey of Behaviour-Aware Methods

Zareie¹,

Sakellariou²

2021

Preprint

View full text Add to dashboard Cite

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“…Following this work, improved greedy algorithms [8,9,13,24], community based approaches [7,23,36], new centrality measures [35,37] and efficient heuristics [15,25,31,45] have been proposed to solve the problem aiming to balance the time complexity of algorithms and the influence propagation and trying to make them scalable to large datasets. In recent years, researchers have studied the influence maximization problem in more complex networks by taking the heterogeneity of individual relationships [18] or multiplexity [42] into consideration.…”

Section: Related Workmentioning

confidence: 99%

Containment of rumor spread in complex social networks

Lan

Giua

2020

Information Sciences

159

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Rumors can propagate at great speed through social networks and produce significant damages. In order to control rumor propagation, spreading correct information to counterbalance the effect of the rumor seems more appropriate than simply blocking rumors by censorship or network disruption. In this paper, a competitive diffusion model, namely Linear Threshold model with One Direction state Transition (LT1DT), is proposed for modeling competitive information propagation of two different types in a same network. The problem of minimizing rumor spread in social networks is explored and a novel heuristic based on diffusion dynamics is proposed to solve this problem under the LT1DT. Experimental analysis on four different networks shows that the novel heuristic outperforms pagerank centrality. By seeding correct information in the proximity of rumor seeds, the novel heuristic performs as well as the greedy approach in scale-free and small-world networks but runs three orders of magnitude faster than the greedy approach.

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“…According to the analysis above, we can extend the native greedy selection algorithm to solve the SATA problem, which can return at least ð1 − 1/eÞapproximation ratio of the optimal result and is widely used to solve the influence maximization problem [29]. Algorithm 1 shows the details of the native greedy selection algorithm.…”

Section: Algorithm Designmentioning

confidence: 99%

Social-Aware Task Allocation in Mobile Crowd Sensing

Zhu

Guo

2020

Wireless Communications and Mobile Computing

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Task allocation is a significant issue in crowd sensing, which trades off the data quality and sensing cost. Existing task allocation works are based on the assumption that there is plenty of users available in the candidate pool. However, for some specific applications, there may be only a few candidate users, resulting in the poor completion of tasks. To tackle this problem, in this paper, we investigate the task allocation problem with the assistance of social networks. We select a subset of users; if a user can not complete the task, he can propagate the task information to his friends. The object of this problem is to maximize the expected number of completed tasks. We prove that the task allocation problem is an NP-hard and submodular problem and then propose a native greedy selection (NGS) algorithm, which selects the user with maximum margin gain in each round. To improve the efficiency of the NGS algorithm, we further propose a fast greedy selection algorithm (FGS), which selects the user who can actually complete the maximum number of tasks. Experimental results show that although FGS gets slightly worse results in terms of the expected number of completed tasks, it can greatly reduce the running time of seed selection.

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Exploring influence maximization in online and offline double-layer propagation scheme

Cited by 24 publications

References 14 publications

Influence Maximization in Social Networks: A Survey of Behaviour-Aware Methods

Influence Maximization in Social Networks: A Survey of Behaviour-Aware Methods

Containment of rumor spread in complex social networks

Social-Aware Task Allocation in Mobile Crowd Sensing

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