You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: While many different message-passing strategies have been proposed, some critical limitations have been uncovered in this class of GNNs. These include the limited expressiveness of GNNs (Xu et al., 2019; Morris et al., 2019), as well as known problems such as over-smoothing (Li et al., 2018, 2019; Chen et al., 2020; Oono & Suzuki, 2020) and over-squashing (Alon & Yahav, 2021).Over-smoothing manifests as all node representations converging to a constant after sufficiently many layers, while over-squashing occurs when messages from distant nodes are not effectively propagated through certain “bottlenecks” in a graph, since too many messages get compressed into a single fixed-length vector. Designing new architectures beyond neighborhood aggregation is thus essential to solve these problems. Message passing graph neural networks have recently been one of the leading methods for graph representation learning. An early seminal example is the GCN (Kipf & Welling, 2017), which was based on performing convolutions on the graph. Gilmer et al. (2017) reformulated the early GNNs into a framework of message passing GNNs, which has since then become the predominant framework of GNNs in use today, with extensive examples (Hamilton et al., 2017; Xu et al., 2019; Corso et al., 2020; Hu et al., 2020b; Veličković et al., 2018; Li et al., 2020a; Yang et al., 2022). However, as mentioned above, they suffer from problems of limited expressiveness, over-smoothing, and over-squashing.

Question: what is limitations of gnns?
There are two most widely adopted limitations of GNNs : over-smoothing and over-squashing.
Over-smoothing is a phenomenon that indicates representations of GNNs get similar to each others as the number of layers increases.
Over-squashing is a difficulty of node representations to contain messages that come from distant neighbors.
(False : The answer can be found only in P0)