There is a growing
trend toward the use of interaction network
methods and algorithms, including community-based detection methods,
in various fields of science. The approach is already used in many
applications, for example, in social sciences and health informatics
to analyze behavioral patterns during the COVID-19 pandemic, protein–protein
networks in biological sciences, agricultural science, economy, and
so forth. This paper attempts to build interaction networks based
on high-entropy alloy (HEA) descriptors in order to discover HEA communities
with similar functionality. In addition, these communities could be
leveraged to discover new alloys not yet included in the data set
without any experimental laboratory effort. This research has been
carried out using two community detection algorithms, the Louvain
algorithm and the enhanced particle swarm optimization (PSO) algorithm.
The data set, which is used in this paper, includes 90 HEAs and 6
descriptors. The results reveal 13 alloy communities, and the accuracy
of the results is validated by the modularity. The experimental results
show that the method with the PSO-based community detection algorithm
can achieve alloy communities with an average accuracy improvement
of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics
of HEAs, for example, their phase composition, could be predicted
by the extracted communities. Also, the HEA phase composition has
been predicted by the proposed method and achieved about 93% precision.