Graph Neural Networks (GNNs) have emerged as a revolutionary paradigm in the
realm of machine learning, offering a transformative approach to dissect
intricate relationships inherent in graph-structured data. The foundational
architecture of most GNNs involves the dissemination of information through
message aggregation and transformation among interconnected nodes, a mechanism
that has demonstrated remarkable efficacy across diverse applications
encompassing node classification, link prediction, and recommendation systems.
Nonetheless, their potential prowess encounters a restraint intrinsic to
scenarios necessitating extensive contextual insights. In certain contexts,
accurate predictions hinge not only upon a node's immediate local surroundings
but also on interactions spanning far-reaching domains. This intricate demand
for long-range information dissemination exposes a pivotal challenge recognized
as "over-squashing," wherein the fidelity of information flow from distant
nodes becomes distorted. This phenomenon significantly curtails the efficiency
of message-passing mechanisms, particularly for tasks reliant on intricate
long-distance interactions. In this comprehensive article, we illuminate the
prevalent constraint of over-squashing pervading GNNs. Our exploration entails
a meticulous exposition of the ongoing efforts by researchers to improve the
ramifications posed by this limitation. Through systematic elucidation, we
delve into strategies, methodologies, and innovations proposed thus far, all
aimed at mitigating the detriments of over-squashing. By shedding light on this
intricately woven issue, we aim to contribute to a nuanced understanding of the
challenges within the GNN landscape and the evolving solutions designed to
surmount them.

对图神经网络（GNN）中普遍存在的过烈抑制问题进行了细致剖析，探讨了研究者目前提出的改善方法和创新，旨在解决 GNN 领域面临的挑战。