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Directional Blindness In Social Network Path-Strength Calculation

IP.com Disclosure Number: IPCOM000230067D
Publication Date: 2013-Aug-16
Document File: 3 page(s) / 22K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are an algorithm and concept that allow the application of a directional-weight to path-strength calculations in order to simulate real-world differences between the information known by Entity A about Entity n, vs. the inverse relationship of Entity n to Entity A. This invention is applied to social networking scenarios.

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Directional Blindness In Social Network Path-Strength Calculation

Currently, path-strength calculations (i.e., Degree-of-Separation calculations) between two entities or individuals are assumed bi-directionally equivalent.

In real-world social networking scenarios, however, this simplification may result in an inaccurate scoring model of the relationships involved.

Examples may be seen in social media/networking sites and online discussion-forums (Example C, below) or observed in certain covert-cell structures (Examples A and B, below), such that:

A. Members of separate cells, controlled by a central individual, know nothing of each other's existence, and/or

B. Cell-members know little detail about a controlling member who is providing instructions, but the controller knows a great deal about the cell-members, and/or

C. A member (A) of a social network may know more about another member (B) than vice-versa, due to the relative amount of data shared by each individual.

The algorithm and concept described herein allow the application of a directional-weight

to path-strength calculations, in order to simulate real-world differences between the

information known by Entity A about Entity n , vs. the inverse relationship of Entity n to Entity A.

The concept is 'Disclosed Compartmentalization', Directional Relationship-blindness in a Social Network. This can be implemented as a 'manual' enhancement to a 'Disclosed Relationship'. Alternatively, the weighting factor can be automated, based upon the amount of known data for each individual. The approach works on the principle of Relationship-Path weighting.

Figure: Standard 'Degrees of Separation' (DSN) calculation

PS=Path-Strength


Normal calculation of PS = PSn * PSn+1 * ... * PSn+x Wherein:


• C Knows B


• B Knows A

1


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• C Knows A at PSn (Standard DSN Calculation) and


• C Knows D at PSn' (Standar...