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20192021
most citedEstimating Node Importance in Knowledge Graphs Using Graph Neural Networks

126 citations · 241 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.LG2020★ 4 cited

Denoising individual bias for a fairer binary submatrix detection

Changlin Wan, Wennan Chang, Tong Zhao +2

Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications. Existing binary matrix factoriza…

cs.LG2020

Geometric All-Way Boolean Tensor Decomposition

Changlin Wan, Wennan Chang, Tong Zhao +2

Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains. Boolean Tensor Decomposition…

cs.LG2020★ 22 cited

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Namyong Park, Andrey Kan, Xin Luna Dong +2

Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of app…

cs.LG2019

Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation

Changlin Wan, Wennan Chang, Tong Zhao +3

Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, e…

cs.LG2019★ 126 cited

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

Namyong Park, Andrey Kan, Xin Luna Dong +2

How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and sem…