Large bibliographic networks are sparse -- the average node degree is small. This is not necessarily true for their product -- in some cases, it can ``explode'' (it is not sparse, increases in time and space complexity). An approach in such cases is to reduce the complexity of the problem by limiting our attention to a selected subset of important nodes and computing with corresponding truncated networks. The nodes can be selected by different criteria. An option is to consider the most important nodes in the derived network -- nodes with the largest weighted degree. It turns out that the weighted degrees in the derived network can be computed efficiently without computing the derived network itself.

翻译：大型文献网络是稀疏的——节点平均度数较小。然而其衍生网络未必如此，某些情况下可能出现“爆炸性增长”（不再稀疏，且时空复杂度增加）。对此类问题的处理策略是，通过限定关注重要节点子集并计算对应截断网络来降低问题复杂度。节点选择可依据不同标准，其中一种方案是选取衍生网络中最具重要性的节点——即加权度数最大的节点。研究表明，无需实际构建衍生网络，即可高效计算出其中各节点的加权度数。