diff --git a/Chapters/14-GNN-GCN/INDEX.md b/Chapters/14-GNN-GCN/INDEX.md
index 3029ffe..04f410b 100644
--- a/Chapters/14-GNN-GCN/INDEX.md
+++ b/Chapters/14-GNN-GCN/INDEX.md
@@ -122,6 +122,8 @@ Take all node embeddings that are in the neighbouroud and do similar steps as th
 
 ## Polynomial Filters
 
+Each polynomial filter is order invariant
+
 ### Graph Laplacian
 
 Let's set an order over nodes of a graph, where $A$ is the adjacency matrix:
@@ -195,4 +197,97 @@ $$
 > So this shows that the degree of the polynomial decides the max number of hops
 > to be included during the filtering stage, like if it were defining a [kernel](./../7-Convolutional-Networks/INDEX.md#filters)
 
-### ChebNet
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+### ChebNet
+
+The polynomial in ChebNet becomes:
+
+$$
+\begin{aligned}
+p_{\vec{w}}(L) &= \sum_{i = 1}^{d} w_{i} T_{i}(\tilde{L}) \\
+T_{i} &= cos(i\theta) \\
+\tilde{L} &= \frac{2L}{\lambda_{\max}(L)} - I_{n}
+\end{aligned}
+$$
+
+- $T_{i}$ is Chebischev first kind polynomial
+- $\tilde{L}$ is a reduced version of $L$ because we divide for its max eigenvalue,
+    keeping it in range $[-1, 1]$. Moreover $L$ ha no negative eigenvalues, so it's
+    positive semi-definite
+
+These polynomials are more stable as they do not explode with higher powers
+
+### Embedding Computation
+
+<!-- TODO: Read PDF 14 Anelli from 81 to 83 -->
+
+## Other methods
+
+- <span style="color:skyblue">Learnable parameters</span>
+- <span style="color:orange">Embeddings of node v</span>
+- <span style="color:violet">Embeddings of neighbours of v</span>
+
+### Graph Convolutional Networks
+
+$$
+\textcolor{orange}{h_{v}^{(k)}} =
+\textcolor{skyblue}{f^{(k)}} \left(
+    \underbrace{\textcolor{skyblue}{W^{(k)}} \cdot
+    \frac{
+        \sum_{u \in \mathcal{N}(v)} \textcolor{violet}{h_{u}^{(k-1)}}
+    }{
+        |\mathcal{N}(v)|
+    }}_{\text{mean of previous neighbour embeddings}} + \underbrace{\textcolor{skyblue}{B^{(k)}} \cdot
+\textcolor{orange}{h_{v}^{(k - 1)}}}_{\text{previous embeddings}}
+\right) \forall v \in V
+$$
+
+### Graph Attention Networks
+
+$$
+\textcolor{orange}{h_{v}^{(k)}} =
+\textcolor{skyblue}{f^{(k)}} \left(
+    \textcolor{skyblue}{W^{(k)}} \cdot \left[
+    \underbrace{
+        \sum_{u \in \mathcal{N}(v)} \alpha^{(k-1)}_{v,u}
+        \textcolor{violet}{h_{u}^{(k-1)}}
+     }_{\text{weighted mean of previous neighbour embeddings}}  +
+        \underbrace{\alpha^{(k-1)}_{v,v}
+        \textcolor{orange}{h_{v}^{(k-1)}}}_{\text{previous embeddings}}
+\right] \right) \forall v \in V
+$$
+
+where
+
+$$
+\alpha^{(k)}_{v,u} = \frac{
+    \textcolor{skyblue}{A^{(k)}}(
+        \textcolor{orange}{h_{v}^{(k)}},
+        \textcolor{violet}{h_{u}^{(k)}},
+    )
+}{
+    \sum_{w \in \mathcal{N}(v)} \textcolor{skyblue}{A^{(k)}}(
+        \textcolor{orange}{h_{v}^{(k)}},
+        \textcolor{violet}{h_{w}^{(k)}},
+    )
+} \forall (v, u) \in E
+$$
+
+### Graph Sample and Aggregate (GraphSAGE)
+
+<!-- TODO: See PDF 14 Anelli from 98 to 102 -->
+
+### Graph Isomorphism Network (GIN)
+
+$$
+\textcolor{orange}{h_{v}^{(k)}} =
+ \textcolor{skyblue}{f^{(k)}}
+\left(
+    \sum_{u \in \mathcal{N}(v)}
+    \textcolor{violet}{h_{u}^{(k - 1)}} +
+    (
+        1 +
+        \textcolor{skyblue}{\epsilon^{(k)}}
+    ) \cdot \textcolor{orange}{h_{v}^{(k - 1)}}
+\right)
+\forall v \in V
+$$
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