利用网络动态性改进链接预测
Leveraging Network Dynamics for Improved Link Prediction
摘要：
链接预测的目的是基于先前观察到的链接的示例来预测未来最有可能发生的连接。一个关键的见解是，在进行链接预测时，显式地对网络动态（链接创建或破坏的频率）进行建模是有用的。在本文中，我们介绍了一个新的监督链接预测框架，RPM（率预测模型）。除了网络相似性度量，RPM还使用预测的链接修改率，使用时间序列数据建模;它在Spark-ML中实现，并使用原始链接分布进行训练，而不是一个小的平衡子集。我们比较使用这个网络动力学模型直接创建时间序列的网络相似性措施。我们的实验表明，RPM，利用预测率，优于使用网络相似性措施，无论是单独或在一个时间序列。
英文摘要：
The aim of link prediction is to forecast connections that are most likely to occur in the future, based on examples of previously observed links. A key insight is that it is useful to explicitly model network dynamics, how frequently links are created or destroyed when doing link prediction. In this paper, we introduce a new supervised link prediction framework, RPM (Rate Prediction Model). In addition to network similarity measures, RPM uses the predicted rate of link modifications, modeled using time series data; it is implemented in Spark-ML and trained with the original link distribution, rather than a small balanced subset. We compare the use of this network dynamics model to directly creating time series of network similarity measures. Our experiments show that RPM, which leverages predicted rates, outperforms the use of network similarity measures, either individually or within a time series.
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。
