Group-Level Influence Maximization with Budget Constraint

Yan, Qian; Huang, Hao; Gao, Yunjun; Lu, Wei; He, Qinming

doi:10.1007/978-3-319-55753-3_39

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…A directed edge from a parent node v i to a child node v j indicates that when v i is infected and v j is uninfected, v i will successfully infect v j with a certain probability (which can be regarded as the edge weight of this directed edge). As a few existing approaches have presented how to calculate the edge weight based on observed infection status results for a specified edge (Yan et al 2017), in this paper, we focus on inferring the unknown directed edge set of the objective network. Our problem can be formulated as follows.…”

Section: Problem Statementmentioning

confidence: 99%

Learning Diffusions without Timestamps

Huang

Yan

Gan

et al. 2019

AAAI

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To learn the underlying parent-child influence relationships between nodes in a diffusion network, most existing approaches require timestamps that pinpoint the exact time when node infections occur in historical diffusion processes. In many real-world diffusion processes like the spread of epidemics, monitoring such infection temporal information is often expensive and difficult. In this work, we study how to carry out diffusion network inference without infection timestamps, using only the final infection statuses of nodes in each historical diffusion process, which are more readily accessible in practice. Our main result is a probabilistic model that can find for each node an appropriate number of most probable parent nodes, who are most likely to have generated the historical infection results of the node. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach.

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Section: Problem Statementmentioning

confidence: 99%

Learning Diffusions without Timestamps

Huang

Yan

Gan

et al. 2019

AAAI

Self Cite

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show abstract

“…As a few existing studies offer proposal for how to calculate edge weights based on observed infection status results [32]. In contrast, we focus on inferring the unknown directed edge set of the objective network.…”

Section: Problem Statementmentioning

confidence: 99%

“…This way, we can efficiently achieve a locally optimal F i . A similar greedy search procedure is used commonly in many other applications, such as influence maximization [29] and classification [32], due to its efficiency and good result quality.…”

Section: H(xmentioning

confidence: 99%

Statistical Inference of Diffusion Networks

Huang

Yan

Chen

et al. 2019

IEEE Trans. Knowl. Data Eng.

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To infer structures in diffusion networks, existing approaches mostly need to know not only the final infection statuses of network nodes, but also the exact times when infections occur. In contrast, in many real-world settings, such as disease propagation, monitoring exact infection times is often infeasible due to a high cost. We investigate the problem of how to learn diffusion network structures based on only the final infection statuses of nodes. Instead of utilizing sequences of timestamps to determine potential parent-child influence relationships between nodes, we propose to find influence relationships with high statistical significance. To this end, we design a probabilistic generative model of the final infection statuses to quantitatively measure the likelihood of potential structures of the objective diffusion network, taking into account network complexity. Based on this model, we can infer an appropriate number of most probable parent nodes for each node in the network. Furthermore, to reduce redundant inference computations, we are able to preclude insignificant candidate parent nodes from being considered during inferencing, if their infections have little correlation with the infections of the corresponding child nodes. Extensive experiments on both synthetic and real-world networks offer evidence that the proposed approach is effective and efficient.

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“…Those who would use social networks to diffuse their message seek to reach as many nodes as possible, and to do so as quickly as possible. The messages to be diffused, though, may be more effective and convincing if they are received from a friend than from the change agent, so there may be a desire to limit the number of initial contacts that are used to "seed" the diffusion [4,12]. There are three important parameters in each diffusion process, the first parameter is the number of seed nodes in a diffusion, the second one is the total time of diffusion and the last one is the total number of nodes that are influenced in diffusion process.…”

mentioning

confidence: 99%

“…They assumed that all the considered parameters of the problem are deterministic while some of them are stochastic in the real world. In addition, almost all of them assumed that the nodes are homogeneous with regard to their activation thresholds, but differing in their out-degrees (e.g., [7,9,[12][13][14]), While nodes in these models may differ in the number of others to whom they have access, the previous research assumed that all nodes utilize all of their social ties. We believe that the more realistic approach is to consider nodes as heterogeneous in their propensity to act as social influencers and considering the probabilistic nature of the problem in proposed model.…”

mentioning

confidence: 99%

A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

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Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.

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Group-Level Influence Maximization with Budget Constraint

Cited by 17 publications

References 14 publications

Learning Diffusions without Timestamps

Learning Diffusions without Timestamps

Statistical Inference of Diffusion Networks

A robust optimization model for influence maximization in social networks with heterogeneous nodes

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