Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models;...

The paper investigates whether one can learn about the degree of climate risk exposure of firms by extracting information about their fundamentals and network connections and applying innovative graph-based artificial intelligence (AI) methodologies. We tackle this problem by creating a...

The evolution of the convergence among the European countries, including both Eurozone as well as non-Eurozone economies, is investigated in this paper. To do so, we construct correlation-based networks and study them by employing the Threshold Weighted-Minimum Dominating Set (TW-MDS) algorithm...

This study proposes hierarchical risk parity portfolios using a new correlation matrix and security selection. We suggest a global motion subtracted correlation matrix, which eliminates the global motion in the cross-correlation matrix. Also, we suggest utilizing the peripheral assets of a...

This research tries to go deeply in the application of multidimensional graph(s) in microeconomics. We are divided into three large sections this research followed by: (i) the visualization of demand and supply curves from a multidimensional perspective; (ii) the visualization of indifference...