Big Data Analysis (BDA) has attracted considerable interest and curiosity from scientists of various fields recently. As big size and complexity of big data, it is pivotal to uncover hidden patterns, bursts of activity, correlations and laws of it. Complex network analysis could be effective method for this purpose, because of its powerful data organization and visualization ability. Besides the general complex software (Gephi), some bibliometrics software (Citespace and SCI 2 ) could also be used for this analysis, due to their powerful data process and visualization functions. This paper presents a comparison of Citespace, SCI 2 and Gephi from the following three aspects: (1) Data preprocessing. Citespace is time-consuming and laborious on merging synonyms. SCI 2 is efficient and suitable for massive data. Both of them could remove duplicate records. However, Gephi lacks these. (2) Network extraction. Citespace pays more attention on temporal analysis, but SCI 2 and Gephi pay less attention on it. Besides, Citespace and SCI 2 could provide pruning algorithms highlight the main structure of social network, but Gephi lacks this. (3) Visualization. In Citespace, co-occurrence network could present time, frequency and betweenness centrality simultaneously; cluster view could label the clusters with phrases. However, SCI 2 and Gephi provide more various layout algorithms to present network. Besides, they have a better edit-ability than Citespace on generated network.
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Yang et al. (2017) studied this question.
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