Authors:
Takeshi Ozeki
;
Teruhiko Kudo
and
Yosuke Takeda
Affiliation:
Sophia University, Japan
Keyword(s):
Adjacency matrix, Multi-modal Analysis, Markov transition, Euclidian Norm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Reduction and Quality Assessment
;
Foundations of Knowledge Discovery in Databases
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
Abstract:
We report a new method of diagnosis of a node in a network by “Point Stimulus Response”. The “Point Stimulus Response” corresponds to the impulse response of the network, that is, the state temporal variation in the Markov transition with the delta-function of initial state. We can evaluate the reaction of the system against a point stimulus such as a point failure. In this report, for the first, we summarize our mathematical platform for analysing complex network system using the adjacency matrix as the transition matrix in Markov transition approximation. On this basis, we formulate the point stimulus response. The location dependence of the point stimulus response is demonstrated in Tokyo Metropolitan Railway Network System. For a concrete example, the total amount of suffered passengers and time response of recovery from a point failure will be discussed depending on the location of point failure in the network system. It can be said that a way to find a point for effective stim
ulus response is one of key approaches for knowledge discovery. However, the real indication or meaning of the point stimulus is in the stage of speculation.
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