Nonparametric Link Prediction in Dynamic Networks
Nonparametric Link Prediction in Dynamic Networks
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发表时间：
2012-06
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通讯作者：
Purnamrita Sarkar;Deepayan Chakrabarti;Michael I. Jordan
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作者：
Purnamrita Sarkar;Deepayan Chakrabarti;Michael I. Jordan
我们提出了一种针对一系列随时间变化的图快照的非参数链接预测算法。该模型根据其端点以及端点周围的本地邻域的特征来预测链接。这允许在图中显示不同类型的社区，每个社区都有自己的动态(例如，社区的增长或缩小)。我们证明了估计器的一致性，并给出了一个基于位置敏感散列的快速实现。用模拟和五个真实世界的动态图表进行的实验表明，我们的表现优于最先进的水平，特别是在存在剧烈波动或非线性的情况下。
We propose a nonparametric link prediction algorithm for a sequence of graph snapshots over time. The model predicts links based on the features of its endpoints, as well as those of the local neighborhood around the endpoints. This allows for different types of neighborhoods in a graph, each with its own dynamics (e.g, growing or shrinking communities). We prove the consistency of our estimator, and give a fast implementation based on locality-sensitive hashing. Experiments with simulated as well as five real-world dynamic graphs show that we outperform the state of the art, especially when sharp fluctuations or nonlinearities are present.