diff --git a/src/layers/pool/eigenpool.jl b/src/layers/pool/eigenpool.jl
index 0d6dd90..c4d18c4 100644
--- a/src/layers/pool/eigenpool.jl
+++ b/src/layers/pool/eigenpool.jl
@@ -7,21 +7,41 @@ using ChemistryFeaturization
 #= Pooling Layer based on
 Graph Convolutional Networks with EigenPooling - Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang
 https://arxiv.org/abs/1904.13107
+
+Since the size of the matrices we are dealing with isn't as huge as the one in the original work,
+let us try using this as a global pooling mechanism instead, i.e., the adjacency matrix of the crystal
+itself would be only "subgraph" from which we "coarsen" to a single node and pool accordingly, which
+in theory would give us the overall graph representation
 =#
 
 # TBD - what other fields would be necessary for the pooling layer itself?
 struct EigenPool
-    pool_func::Function
+    pool::Function # pooling operator applied over the adjacency matrix
     function EigenPool()
-        new(eigen_pool_func)
+        new(eigen_pooling)
     end
 end
 
-function eigen_pool_func(adj_graph::Matrix{<:Real}, features::Matrix{<:Real})
-    # graph_coarsening on adj_graph
-    # apply EigenPooling operator on the pooled features to generate new node representations corresponding to coarsened graph
+function eigen_pooling(graph::Matrix{<:Real}, features::Matrix{<:Real})
+    L = Atoms.normalized_laplacian(graph) # get the laplacian matrix
+    L_eigen_vectors = eigvecs(L)    # find eigen vectors for L
+    result = Vector()
+
+    for i = 1:size(L_eigen_vectors)[1]
+        L_i = L_eigen_vectors[:, i]
+        push!(result, L_i'*features)
+    end
+
+    # using an agreeable H and then return H elements of result hcatt-ed into a single 1xdH vector
+
 end
 
+#=
+ = Given an adjacency graph and the corresponding matrix of node features, return the
+ = pooled node features which is the final graph representation fed to the dense layer
+ = The adj_graph would be `FeaturizedAtoms.atoms` and features would be the same as usual
+ =#
 function (m::EigenPool)(adj_graph::Matrix{<:Real}, features::Matrix{<:Real})
-    return m.pool_func(adj_graph, features)
+    features = m.pool(adj_graph, features)
+    return adj_graph, features
 end