What does "Big Data" tell? A network analysis approach to the justice and development party’s role performance in the Middle East between 2015 and 2020

In this paper, we exploited big data (The Global Database Events, Language and Tone - GDELT) by utilizing network analysis to elaborate on the Justice and Development Party’s (JDP) Middle East (ME) policy for 2015 and 2020 - our conceptual framework built on the role theory. We identified two dynamics - the positioning of the "Transatlantic" link in Turkish Foreign Policy's (TFP) orientation and the shape and politics of the JDP elites' conception of activism - based on which we developed two hypotheses to conceptualize the JDP's role performance for the period: 1) There was a mismatch between national role conceptions and systemic role prescriptions for Turkey in the period of analysis; 2) This mismatch led the appeal of partnership with non-Western actors to rise. We utilized network analysis by exploiting the GDELT big dataset to test our hypotheses empirically. The empirical findings proved the validity of our conceptual arguments

Network design with weighted degree constraints

In an undirected graph G = (V, E) with a weight function w : E × V → Q+, the weighted degree dw(v;E) of a vertex v is defined as Σ{w(e, v)｜e ∈ E incident to v}. In this paper, we consider a network design problem which has upper-bounds on weighted degrees of vertices as its constraints while the objective is to compute a minimum cost graph with a prescribed connectivity. We propose bi-criteria approximation algorithms based on the iterative round-ing, which has been successfully applied to the degree-bounded network design problem. A problem of minimizing the maximum weighted degree of vertices is also discussed