  GNNs are widely used to solve various tasks including node classification and
link prediction. Most of the GNN architectures assume the initial embedding to
be random or generated from popular distributions. These initial embeddings
require multiple layers of transformation to converge into a meaningful latent
representation. While number of layers allow accumulation of larger
neighbourhood of a node it also introduce the problem of over-smoothing. In
addition, GNNs are inept at representing structural information. For example,
the output embedding of a node does not capture its triangles participation. In
this paper, we presented a novel feature extraction methodology GraphViz2Vec
that can capture the structural information of a node's local neighbourhood to
create meaningful initial embeddings for a GNN model. These initial embeddings
helps existing models achieve state-of-the-art results in various
classification tasks. Further, these initial embeddings help the model to
produce desired results with only two layers which in turn reduce the problem
of over-smoothing. The initial encoding of a node is obtained from an image
classification model trained on multiple energy diagrams of its local
neighbourhood. These energy diagrams are generated with the induced sub-graph
of the nodes traversed by multiple random walks. The generated encodings
increase the performance of existing models on classification tasks (with a
mean increase of $4.65\%$ and $2.58\%$ for the node and link classification
tasks, respectively), with some models achieving state-of-the-art results.
