<p>There are a bunch of algorithms out there to convert Graph into feature vectors. Two famous examples are:</p>

<ul>
<li><a href="https://snap.stanford.edu/node2vec/" rel="nofollow noreferrer">Node2vec</a> from Stanford</li>
<li><a href="https://arxiv.org/abs/1403.6652" rel="nofollow noreferrer">Deepwalk</a> from Univ. Sherbrooke</li>
</ul>

<p>Their implementation exists on GitHub. </p>

<p>The underlying idea between these methods are almost the same: 1) Random walk 2) generate some sequences 3) word2vec (skip-gram) or other DL methods 4) use output as a feature vector in other Task.</p>

<p><a href="https://i.stack.imgur.com/FwCWf.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/FwCWf.png" alt="enter image description here"></a></p>
Using graph properties as an input in Neural Network
<p>I have almost 1000 of pandas DataFrame, which I converted into graphs. Now I can access edges and nodes of those graphs. For one DataFrame it looks like following:</p>

<pre><code>nx.edges(FG)

Out[59]: EdgeView([('Dy0O7', 'Dy1O6'), ('Dy0O7', 'Dy2O6'), ('Dy0O7', 'Dy3O7'), ('Dy0O7', 'Dy4O6'), ('Dy1O6', 'Dy3O7'), ('Dy1O6', 'Dy5O6'), ('Dy2O6', 'Dy4O6'), ('Dy3O7', 'Dy4O6'), ('Dy3O7', 'Dy5O6')])

nx.nodes(FG)

Out[61]: NodeView(('Dy0O7', 'Dy1O6', 'Dy2O6', 'Dy3O7', 'Dy4O6', 'Dy5O6'))
</code></pre>

<p>Also I can have adjancy view, which gives the information about connected nodes with corresponding weights.</p>

<pre><code>FG.adj

Out[64]: AdjacencyView({'Dy0O7': {'Dy1O6': {'weight': 3.0}, 'Dy2O6': {'weight': 1.0}, 'Dy3O7': {'weight': 2.0}, 'Dy4O6': {'weight': 1.0}}, 'Dy1O6': {'Dy0O7': {'weight': 3.0}, 'Dy3O7': {'weight': 1.0}, 'Dy5O6': {'weight': 1.0}}, 'Dy2O6': {'Dy0O7': {'weight': 1.0}, 'Dy4O6': {'weight': 1.0}}, 'Dy3O7': {'Dy0O7': {'weight': 2.0}, 'Dy1O6': {'weight': 1.0}, 'Dy4O6': {'weight': 3.0}, 'Dy5O6': {'weight': 1.0}}, 'Dy4O6': {'Dy0O7': {'weight': 1.0}, 'Dy2O6': {'weight': 1.0}, 'Dy3O7': {'weight': 3.0}}, 'Dy5O6': {'Dy1O6': {'weight': 1.0}, 'Dy3O7': {'weight': 1.0}}})
</code></pre>

<p>I want to use such graph properties as an input in Machine learning algorithm such as NN, How can we do that?</p>