Abstract: The Comprehension Normalization Method for Networks, compares edge construction to edge construction between networks looking for underlying rules/forces in common between two edge constructions. It begins with the clusters from two or more networks as the two or more sides of the comparison and it uses the membership of nodes united by the cluster as proxies for the qualities of the underlying forces. If there are underlying forces in common between the networks, the method will group the original clusters into larger metaclusters of the rules in common.

Abstract: The Comprehension Normalization Method of the present disclosure exploits the differences in the meanings of words or ideas between Big Data sets to build insight. When the comprehension normalization method is performed between two big data sets, both data sets take turns rephrasing the material of the other data set in their own language of understanding. The act of rephrasing a foreign idea connects the data within the set doing the rephrasing in a way it had not been connected before. After two sets take turns rephrasing the data within, both sets will become more connected than ever before and more insightful to the researcher.

Abstract: The research tool is a series of vertical and horizontal engines where the vertical collects, isolates data and the horizontal clusters by metric. The tool uses a series of verticals and horizontals in a combination which allows for the isolation of causal factors by comparisonability.

Abstract: The Comprehension Normalization Method of the present disclosure exploits the differences in the meanings of words or ideas between Big Data sets to build insight. When the comprehension normalization method is performed between two big data sets, both data sets take turns rephrasing the material of the other data set in their own language of understanding. The act of rephrasing a foreign idea connects the data within the set doing the rephrasing in a way it had not been connected before. After two sets take turns rephrasing the data within, both sets will become more connected than ever before and more insightful to the researcher.

Abstract: The research tool is a series of vertical and horizontal engines where the vertical collects, isolates data and the horizontal clusters by metric. The tool uses a series of verticals and horizontals in a combination which allows for the isolation of causal factors by comparisonability.