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Influence maximization (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. However, previous research has mostly focused on individual information propagation, neglecting the simultaneous and interactive dissemination of multiple information items. In reality, when users encounter a piece of information, such as a smartphone product, they often associate it with related products in their minds, such as earphones or computers from the same brand. Additionally, information platforms frequently recommend related content to users, amplifying this cascading effect and leading to multiplex influence diffusion.This paper first formulates the Multiplex Influence Maximization (Multi-IM) problem using multiplex diffusion models with an information association mechanism. In this problem, the seed set is a combination of influential users and information. To effectively manage the combinatorial complexity, we propose Graph Bayesian Optimization for Multi-IM (GBIM). The multiplex diffusion process is thoroughly investigated using a highly effective global kernelized attention message-passing module. This module, in conjunction with Bayesian linear regression (BLR), produces a scalable surrogate model. A data acquisition module incorporating the exploration-exploitation trade-off is developed to optimize the seed set further.Extensive experiments on synthetic and real-world datasets have proven our proposed framework effective. The code is available at https://github.com/zirui-yuan/GBIM.
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Influence Propagation for Linear Threshold Model with Graph Neural Networks
Influence propagation is a network phenomenon governing how information is diffused in a network. With the advent of deep learning, there has been growing interest in applying graph neural networks to extract salient feature representation of the nodes for a variety of network mining tasks, such as forecasting the virality of information cascade. Given the importance of social influence, this paper presents a novel deep learning framework called IP-GNN for simulating the information propagation process in a complex network and learning a node representation that embeds information about the diffusion process under the linear threshold model. Our framework employs a modified graph convolutional network architecture with adaptive diffusion kernel to capture long-range propagation of information along with an entropy-regularized mixture of loss functions to ensure accurate prediction and faster convergence of the learning algorithm. Experimental results on 4 real-world datasets show that the model accurately mimics the output of the linear threshold model, achieving an average accuracy that exceeds 90\% on all datasets.
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- Award ID(s):
- 1939368
- PAR ID:
- 10495965
- Editor(s):
- Jihe Wang, Yi He
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-8164-1
- Page Range / eLocation ID:
- 1141 to 1148
- Format(s):
- Medium: X
- Location:
- Shanghai, China
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDMW60847.2023.00149
