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Abstract

A computer-implemented method comprising a mathematical formulation of the social energy flow of Karma and a machine learning framework combining mathematical graph computation, which form a global sentiment aggregator for the measurement of social energy flows and which is formulated as a data processing, filtering and sampling framework which fuses structural, interpretable graph machine learning with graph neural networks and introduces a Graph Attention Mechanism (GAM) whereby machine learning guides an interpretable graph computation substrate in order to generate general, structured predictions for the past, present and future. This enables the framework to handle large data sets on a global scale in the graph neural network while affording interpretability within the less scalable graph computational substrate which in turn optimizes the use of memory, computer storage and processing power over traditional designs, thereby making global-scale computations on commodity hardware feasible.

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First Claim