ICML 2025

Implicit Degree Bias in the Link Prediction Task
Rachith Aiyappa 1 Xin Wang 2 Munjung Kim 1 Ozgur Can Seckin 1 Yong-Yeol Ahn 1 Sadamori Kojaku 2
Abstract
Link prediction—the task of distinguishing ac-
tual hidden edges from random unconnected node
pairs—is a quintessential task in graph machine
learning. Despite being widely accepted as a uni-
versal benchmark and a downstream task for rep-
resentation learning, its validity is seldom ques-
tioned. Here, we show that the common edge
sampling procedure in link prediction introduces
an implicit bias toward high-degree nodes and
produces a skewed evaluation that favors methods
overly reliant on node degree, to the extent that
a “null” method based solely on node degree can
nearly match optimal performance. To address
this, we propose a degree-corrected link predic-
tion task that offers a more accurate assessment
that aligns better with performance in recommen-
dation tasks. Finally, we demonstrate that this
degree-corrected benchmark can more effectively
train graph machine-learning models by reduc-
ing overfitting to node degrees and facilitating the
learning of relevant structures in graphs.
1. Introduction
Standardized benchmarks like ImageNet (Deng et al., 2009;
Krizhevsky et al., 2012) and SQuAD (Rajpurkar et al., 2016;
2018) play a pivotal role in driving progress in machine
learning by fostering competition through setting clear, mea-
surable goals. In graph machine learning, a core benchmark
is link prediction, which involves identifying missing edges
in a graph, with diverse applications including the recom-
mendations of friends and contents (Huang et al., 2005;
Kunegis & Lommatzsch, 2009; Menon & Elkan, 2011;
Wang et al., 2014), knowledge discoveries (Bordes et al.,
*Equal contribution
1Center for Complex Networks and Sys-
tems Research, Luddy School of Informatics, Computing, and
Engineering, Indiana University, Bloomington, IN, USA 2School
of Systems Science and Industrial Engineering, Binghamton Uni-
ver...