Link Prediction of Artificial Intelligence Concepts using Low Computational Power

This paper presents an approach proposed for the "Science4cast" 2021 competition, organized by the Institute of Advanced Research in Artificial Intelligence, whose main goal was to predict the likelihood of future associations between machine learning concepts in a semantic network. The developed methodology corresponds to a solution for a scenario of availability of low computational power only, exploiting the extraction of low order topological features and its incorporation in an optimized classifier to estimate the degree of future connections between the nodes. The reasons that motivated the developed methodologies will be discussed, as well as some results, limitations and suggestions of improvements.

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References (15)

07CI[v1,y1], CI[v2,y1], CI[v1,y2], CI[v2,y2]
08Resource allocation index (RA): sum of the inverse degree of the common neighbors of the nodes in the pair.
09Adamic-Adar
10Total number of neighbors (TN): sum of the individual degrees of the nodes in the pair
11Preferential attachment index (PA): product of the degree centrality of each node in the pair.
12coefficient (JCCN

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