scholarly journals Refining BERT Embeddings for Document Hashing via Mutual Information Maximization

2021 ◽  
Author(s):  
Zijing Ou ◽  
Qinliang Su ◽  
Jianxing Yu ◽  
Ruihui Zhao ◽  
Yefeng Zheng ◽  
...  
Keyword(s):  
Mutual Information ◽  
Information Maximization ◽  
Mutual Information Maximization

scholarly journals Relation Representation Learning Via Signed Graph Mutual Information Maximization for Trust Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 115
Author(s):  
Yongjun Jing ◽  
Hao Wang ◽  
Kun Shao ◽  
Xing Huo
Keyword(s):  
Mutual Information ◽  
Representation Learning ◽  
Signed Graph ◽  
Trust Prediction ◽  
Trust Networks ◽  
Trust Relation ◽  
Information Maximization ◽  
Real World Datasets ◽  
Negative Links ◽  
Mutual Information Maximization

Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

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