基于自我网络的同现数据相似性度量
Measuring similarity in co-occurrence data using ego-networks
摘要：
共现关联在许多经验数据中被广泛观察到。挖掘共现数据中的信息对于促进我们对诸如社交网络、生态系统和大脑网络等系统的理解至关重要。度量实体的相似性是一项重要的任务，通常可以使用基于网络的方法来实现。在这里，我们表明基于聚合网络的传统方法可能会带来不必要的间接关系。为了解决这个问题，我们提出了一种基于每个实体的自我网络的相似性度量，它有效地考虑了一个实体的中心性从一个自我网络到另一个自我网络的变化。该指标计算简单，物理意义明确.使用两个不同的数据集，我们比较了新的指数与其他现有的。我们发现，新的索引优于传统的基于网络的相似性度量，它有时可以超过嵌入方法。与此同时，新指数的测量与其他方法的测量相关性较弱，因此提供了一个不同的维度来量化同现数据中的相似性。总之，我们的工作使基于网络的相似性度量的扩展，并可以潜在地应用于几个相关的任务。
英文摘要：
The co-occurrence association is widely observed in many empirical data. Mining the information in co-occurrence data is essential for advancing our understanding of systems such as social networks, ecosystem, and brain network. Measuring similarity of entities is one of the important tasks, which can usually be achieved using a network-based approach. Here we show that traditional methods based on the aggregated network can bring unwanted in-directed relationship. To cope with this issue, we propose a similarity measure based on the ego network of each entity, which effectively considers the change of an entity's centrality from one ego network to another. The index proposed is easy to calculate and has a clear physical meaning. Using two different data sets, we compare the new index with other existing ones. We find that the new index outperforms the traditional network-based similarity measures, and it can sometimes surpass the embedding method. In the meanwhile, the measure by the new index is weakly correlated with those by other methods, hence providing a different dimension to quantify similarities in co-occurrence data. Altogether, our work makes an extension in the network-based similarity measure and can be potentially applied in several related tasks.
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。
