InDNI: An Infection Time Independent Method for Diffusion Network Inference

Chen, Guoxin; Wang, Yongqing; Shao, Jiangli; Shi, Boshen; Shen, Huawei; Cheng, Xueqi

doi:10.1007/978-3-031-24755-2_6

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-24755-2_6

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InDNI: An Infection Time Independent Method for Diffusion Network Inference

Guoxin Chen

Yongqing Wang

Jiangli Shao

et al.

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“…generalization algorithms on graphs [7,26]. Moreover, distribution shift on graphs can exist at the feature level (e.g., node features) and the topology level (e.g., graph structural properties), which may arise from different types of data biases respectively [26], rendering more challenges for OOD generalization on graphs.…”

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confidence: 99%

Causality and Independence Enhancement for Biased Node Classification

Chen,

Wang,

Guo

et al. 2023

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

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Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias. However, anticipating the type of bias in advance is extremely challenging, and designing models solely for one specific type may not necessarily improve overall generalization performance. Moreover, limited research has focused on the impact of mixed biases, which are more prevalent and demanding in real-world scenarios. To address these limitations, we propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs). Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations through the backdoor adjustment. Meanwhile, independence constraint is introduced to improve the discriminability and stability of causal and spurious features in complex biased environments. Essentially, CIE eliminates different types of data biases from a unified perspective, without the need to design separate methods for each bias as before. To evaluate the performance under specific types of data biases, mixed biases, and

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mentioning

confidence: 99%

Causality and Independence Enhancement for Biased Node Classification

Chen,

Wang,

Guo

et al. 2023

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

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show abstract

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InDNI: An Infection Time Independent Method for Diffusion Network Inference

Cited by 1 publication

References 15 publications

Causality and Independence Enhancement for Biased Node Classification

Causality and Independence Enhancement for Biased Node Classification

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