diff --git a/src/layers/normalise.jl b/src/layers/normalise.jl
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+# Implementation of normalization layers for GraphNeuralNetworks
+
+@doc raw"""
+    PairNorm(scale_value; [scale_individually])
+
+PairNorm layer from paper [PairNorm: Tackling Oversmoothing in GNNs](https://arxiv.org/abs/1909.12223)
+
+Performs the operation(normalization)
+```math
+\mathbf{x}_i^c &= \mathbf{x}_i - \frac{1}{n}
+        \sum_{i=1}^n \mathbf{x}_i \\
+
+        \mathbf{x}_i^{\prime} &= s \cdot
+        \frac{\mathbf{x}_i^c}{\sqrt{\frac{1}{n} \sum_{i=1}^n
+        {\| \mathbf{x}_i^c \|}^2_2}}
+```
+
+The input to this layer is the output from GNN layers
+
+# Arguments
+
+- `scale_value`: Scaling factor `s` used in normalisation. Default `1.0`
+- `scale_individually`: If set to `true`, will compute the scaling step as
+
+```math
+\mathbf{x}^{\prime}_i = s \cdot
+            \frac{\mathbf{x}_i^c}{{\| \mathbf{x}_i^c \|}_2}
+```
+Default `false`
+
+- `ϵ` : Small value added in the denominator for numerical stability. Default `1f-5`
+
+# Examples
+
+```julia
+# create data
+s = [1,1,2,3]
+t = [2,3,1,1]
+g = GNNGraph(s, t)
+x = randn(Float32, 3, g.num_nodes)
+scale_value = 1.0
+
+# create layer
+l = GCNConv(3 => 5)
+pn = PairNorm(scale_value)
+
+# forward pass of GCN
+y = l(g, x)       # size:  5 × num_nodes
+
+# forward pass of PairNorm
+ȳ = pn(y)
+```
+
+"""
+struct PairNorm{V, N}
+    scale_value::V
+    ϵ::N
+    scale_individually::Bool
+end
+
+@functor PairNorm
+
+function PairNorm(scale_value::Real=1.0f0; scale_individually::Bool=false, eps::Real=1f-5, ϵ=nothing)
+    ε = _greek_ascii_depwarn(ϵ => eps, :BatchNorm, "ϵ" => "eps")
+    return PairNorm(scale_value, ε, scale_individually)
+end
+
+function (PN::PairNorm)(x::AbstractArray)
+    xm = mean(x, dims=1)
+    x = x .- xm
+    if PN.scale_individually
+        return (PN.scale_value .* x) ./ (PN.ϵ .+ [norm(x[i,:]) for i in axes(x,1)])
+    else
+        return (PN.scale_value .* x) ./ (PN.ϵ + √(mean(sum(x.^2, dims=2))))
+    end
+end
+
+Base.show(io::IO, pn::PairNorm) = print(io, "PairNorm(", pn.scale_value, ")")
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