A Community Detection-based Approach in Social Networks to Improve the Equation Analysis in the Material Science

Nowadays, high-entropy alloys (HEAs) are a popular domain for researchers which is improved performance by using machine learning (ML). HEAs are formed at least five main elements with close or equal size which is depend on their size and type of elements to extend physical and mechanical features. The ML approach has many applications in various fields. Social network analysis (SNA) is one of the ML tools that is used graph theory. Each graph consists of a number of nodes and edges that each node has its own descriptors. The studies done so far has not used the high-entropy alloys network dataset based on the similarity of content and structural features of each compound. In this paper, a new method is proposed that generalized SNA tools to metallurgical and materials engineering. The proposed method is investigated the HEAs descriptors, in which HEAs descriptors similarity are calculated and the HEAs interaction network is created. The groups have been extracted by Louvain algorithm which each group called cluster. The clusters have alloys with similar properties. The experimental results shown high quality clusters that will be effective in predicting the compounds functionality and discovering new compounds and descriptors. The modularity criterion indicates the quality of the clusters, is about 0.713.