![]() Esmaeilzadeh Dilmaghani, Saharnaz ![]() ![]() ![]() in Heuristics for Optimization and Learning (2020) We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we ... [more ▼] We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we propose a method to convert relational data to a set of networks using a single parameter, called Linkage Threshold (LT). To analyze the impact of the LT-value, we apply standard network metrics such as network density and centrality measures on each network produced. The feasibility and impact of our approach are demonstrated by using a real-world collaboration data set from an established research institution. We show how the produced network layers can reveal insights and patterns by presenting a correlation matrix. [less ▲] Detailed reference viewed: 142 (22 UL) |
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