Speaker
Description
Information centrality is a vertex measure of a graph, which is a harmonic mean of effective conductances defined between a given vertex and all other connected vertices. As the reciprocal of effective resistance, effective conductance serves as a higher-order connectivity measure and has a connection with a renowned mathematical data analysis technique, spectral embedding, by the graph Laplacian. The connection suggests possibility of data analysis with information centrality, by considering a data point as a vertex in a graph formed by data points. Depending on the objective, one can fix a graph and calculate an information centrality of a temporarily appended test point and consider it as an affinity measure of the test point to the data points forming the graph. This metric, which we may term test information centrality, can be interpreted as a non-linear affinity criterion for identifying normal data points in outlier detection tasks. In this talk, we will look into a demonstration of this approach to outlier detection and continuity result of test effective conductance.