加权度与截断的派生书目网络
Weighted degrees and truncated derived bibliographic networks
AI总结：
针对大型书目网络乘积可能导致的复杂度爆炸问题，提出通过选定最大加权度的重要节点构建截断网络来降低复杂度，并实现无需构建派生网络即可高效计算加权度。
摘要：
大型书目网络是稀疏的——平均节点度很小。但其乘积并不一定如此——在某些情况下，它可能会“爆炸”（不稀疏，在时间和空间复杂度上都会增加）。在这种情况下，一种方法是通过将注意力限制在选定的重要节点子集上，并在相应的截断网络上进行计算，来降低问题的复杂度。可以通过不同的标准来选择节点。一种选择是考虑派生网络中最重要的节点——即具有最大加权度的节点。事实证明，无需计算派生网络本身，就能高效地计算出派生网络中的加权度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
