在协作网络中使用拓扑特征和层次表征消除名称的歧义
On the use of topological features and hierarchical characterization for disambiguating names in collaborative networks
摘要：
复杂系统的许多特征现在可以通过应用统计物理学方法将其视为社交网络来揭示。然而，分析的能力可能受到名称中存在的歧义的限制，例如，这是由协作网络中的同音异义引起的。在本文中，我们表明，当考虑较长距离的连接，而不是只看一个节点在协作网络中的近邻，以提高区分homebacks作者的能力。在连接中使用第三层次时，获得了优化结果。此外，合理的作者之间的区别，也可以实现后，使用模式识别策略的数据产生的合作网络的拓扑结构。这些结果是通过从arXiv存储库中的论文中获得的网络获得的，其中故意引入了同音异义，以使用受控的可靠数据集来测试这些方法。在所有情况下，使用了几种监督和无监督机器学习方法，导致了相同的总体结果。使用更深层次和网络拓扑结构的适用性被证实与一个真正的电影演员的数据库，与额外的发现，区分能力可以进一步提高，通过结合拓扑特征和远程连接的协作网络。
英文摘要：
Many features of complex systems can now be unveiled by applying statistical physics methods to treat them as social networks. The power of the analysis may be limited, however, by the presence of ambiguity in names, e.g., caused by homonymy in collaborative networks. In this paper we show that the ability to distinguish between homonymous authors is enhanced when longer-distance connections are considered, rather than looking at only the immediate neighbors of a node in the collaborative network. Optimized results were obtained upon using the 3rd hierarchy in connections. Furthermore, reasonable distinction among authors could also be achieved upon using pattern recognition strategies for the data generated from the topology of the collaborative network. These results were obtained with a network from papers in the arXiv repository, into which homonymy was deliberately introduced to test the methods with a controlled, reliable dataset. In all cases, several methods of supervised and unsupervised machine learning were used, leading to the same overall results. The suitability of using deeper hierarchies and network topology was confirmed with a real database of movie actors, with the additional finding that the distinguishing ability can be further enhanced by combining topology features and long-range connections in the collaborative network.
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