  Quantitatively explaining the strength of arguments under gradual semantics
has recently received increasing attention. Specifically, several works in the
literature provide quantitative explanations by computing the attribution
scores of arguments. These works disregard the importance of attacks and
supports, even though they play an essential role when explaining arguments'
strength. In this paper, we propose a novel theory of Relation Attribution
Explanations (RAEs), adapting Shapley values from game theory to offer
fine-grained insights into the role of attacks and supports in quantitative
bipolar argumentation towards obtaining the arguments' strength. We show that
RAEs satisfy several desirable properties. We also propose a probabilistic
algorithm to approximate RAEs efficiently. Finally, we show the application
value of RAEs in fraud detection and large language models case studies.
