Link Prediction on Latent Heterogeneous Graphs

Nguyen, Trung‐Kien; Liu, Zemin

doi:10.1145/3543507.3583284

Proceedings of the ACM Web Conference 2023 2023

DOI: 10.1145/3543507.3583284

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Link Prediction on Latent Heterogeneous Graphs

Trung‐Kien Nguyen

Zemin Liu

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2024

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Cited by 2 publications

References 26 publications

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Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling

Abdallah,

Afandi,

Kalnis

et al. 2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling

Abdallah,

Afandi,

Kalnis

et al. 2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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Diffusion-based Negative Sampling on Graphs for Link Prediction

Nguyen,

Fang

2024

Proceedings of the ACM Web Conference 2024

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Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. To address these issues, we investigate a novel strategy of multi-level negative sampling that enables negative node generation with flexible and controllable "hardness" levels from the latent space. Our method, called Conditional Diffusion-based Multi-level Negative Sampling (DMNS), leverages the Markov chain property of diffusion models to generate negative nodes in multiple levels of variable hardness and reconcile them for effective graph link prediction. We further demonstrate that DMNS follows the sub-linear positivity principle for robust negative sampling. Extensive experiments on several benchmark datasets demonstrate the effectiveness of DMNS. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Link Prediction on Latent Heterogeneous Graphs

Cited by 2 publications

References 26 publications

Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling

Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling

Diffusion-based Negative Sampling on Graphs for Link Prediction

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