Sadamori Kojaku — accepted AI-conference papers

2025

Implicit degree bias in the link prediction task

Rachith Aiyappa, Xin Wang, Munjung Kim, Ozgur Can Seckin, Yong-Yeol Ahn, Sadamori Kojaku

ICML 2025poster

Link prediction---a task of distinguishing actual hidden edges from random unconnected node pairs---is one of the quintessential tasks in graph machine learning. Despite being widely accepted as a universal benchmark and a downstream task for representation learning, the link prediction benchmark's…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2021

Residual2Vec: Debiasing graph embedding with random graphs

Sadamori Kojaku, Jisung Yoon, Isabel Constantino, Yong-Yeol Ahn

NeurIPS 2021poster

Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods hinge on a sampling of context nodes based on random walks. However, random walks can be a biased sampler due to the structural properties of…

Cited by 29 Source ↗PDF ↗code ↗Scholar ↗