After implementing the spreading activation method, we knew that spreading activation representation is similar to a graph where we have nodes and links but we could not see that graph… We decided to visualize the model using a graph so that would could add power of explanation. This is helpful because adding visualization allow us to see the whole process that our agent followed to create the output. Now, we can pinpoint the exact route followed by our agent to convert the inputs to the outputs.
We used an awesome library called D3.js. This library allowed us to visualize our model easily. So using visualization the teams and the features were all represented as nodes in a network. Two additional nodes namely, Win and Loss were added to represent the sinks of the network. Thus, while the teams represented the source where the activation started from, the Win and Loss nodes represented the sinks where the activation finally ended. Subsequently, teams were connected to features to represent the considerations taken into account by the interviewees. As mentioned before, the features were linked to the Win or the Loss node in the network and these links were weighted with the values.
Figure below shows a network representation with the nodes as teams and features. In the figure, the game under consideration is Spain vs Belgium. Thus, the nodes representing Spain and Belgium are highlighted in blue along with an increase in size of the node marker. Further, the Win and the Loss node are highlighted in green and red, respectively.
The point that I would like to make in this post is that before adding visualization to our method we only had the result of our prediction which was which team will win or draw. However, after adding the visualization we were able to have a better reasoning of why an specific team will win or loss a game. Visualization was a perfect explanatory feature that we added to our system. Probably I would consider using visualization for all the projects that require a feature that helps with explanation. This is a very strong feature to add to our systems.