The Prospective Threat Vector of a Bounded and Controllable Optimized Computational Approach for Spatio-Temporal Knowledge Graph Completion
S. Chan · 2024
There is a presumption by some that the removal of certain materials from online repositories can increase “security through obscurity.” This potentially specious reasoning (as snapshots of these materials may still be available via various digital preservation archives on the surface web as well as the dark web) tends to also be coupled with an underestimation regarding the State-of-the-Art (SOTA) for Spatio-Temporal Knowledge Graph Completion (STKGC) (i.e., the positing of missing nodes, links, etc.) and the possibility of effectively computing the minimum/optimal Control Energy Cost (CEC) for controlling a Large-Scale Complex Network (LSCN). Prospective vulnerable Real World Scenario (RWS) LSCN include power grids and Artificial Intelligence (AI) compute clusters. This paper discusses a prototype Key Node Identification/Assessment (KNIA) module, which successfully undertook STKGC for a power grid and computed the optimal number of Control Signals (CSopt) operating on an optimal number of Key Control Driver Nodes (KCDNopt) at an optimal CEC (CECopt) over an optimal Elongated Temporal Span (ETSopt) so as to constitute prospective meaningful control over the involved digital rendition LSCN. A bespoke architectural construct, in the form of a Hypergraph-Induced Infimal Convolutional Manifold Neural Network (H-IICMNN), was utilized to resolve the aforementioned KNIA-related CS-KCDN-CEC-ETS optimality problems. By discerning these key pathways, defensive bulwarks for mitigation against certain prospective threat vectors can be formulated and instantiated.