  The recent work known as Segment Anything (SA) has made significant strides
in pushing the boundaries of semantic segmentation into the era of foundation
models. The impact of SA has sparked extremely active discussions and ushered
in an encouraging new wave of developing foundation models for the diverse
tasks in the Euclidean domain, such as object detection and image inpainting.
Despite the promising advances led by SA, the concept has yet to be extended to
the non-Euclidean graph domain. In this paper, we explore a novel Segment
Non-Euclidean Anything (SNA) paradigm that strives to develop foundation models
that can handle the diverse range of graph data within the non-Euclidean
domain, seeking to expand the scope of SA and lay the groundwork for future
research in this direction. To achieve this goal, we begin by discussing the
recent achievements in foundation models associated with SA. We then shed light
on the unique challenges that arise when applying the SA concept to graph
analysis, which involves understanding the differences between the Euclidean
and non-Euclidean domains from both the data and task perspectives. Motivated
by these observations, we present several preliminary solutions to tackle the
challenges of SNA and detail their corresponding limitations, along with
several potential directions to pave the way for future SNA research.
Experiments on five Open Graph Benchmark (OGB) datasets across various tasks,
including graph property classification and regression, as well as multi-label
prediction, demonstrate that the performance of the naive SNA solutions has
considerable room for improvement, pointing towards a promising avenue for
future exploration of Graph General Intelligence.
