The h-index is one of the most important bibliographic measures used to assess the performance of researchers. Van Bevern et al. [Artif. Intel., in press] showed that, despite computational worst-case hardness results, substantial manipulation of the h-index of Google Scholar author profiles is possible by merging articles. Complementing previous work, we study the opposite operation, the splitting of articles, which is arguably the more natural operation for manipulation and which is also allowed within Google Scholar. We present numerous results on computational complexity (from linear-time algorithms to parameterized hardness results) and empirically indicate that at least small improvements of the h-index by splitting merged articles are easily achievable.
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