Random Walk with Wait and Restart on Document Co-citation Network for Similar Document Search
Random Walk with Wait and Restart on Document Co-citation Network for Similar Document Search
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发表时间：
2014
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通讯作者：
Masaki Eto
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作者：
Masaki Eto
计算图中节点之间相似性的最新算法之一是重新启动随机游走（RWR）。然而，在用于相似文档搜索的文档共引网络上，计算转移概率仍然是困难的。为了解决这个问题，本文提出了一种随机游走等待和重新启动（RWWR）算法，其中包含一个新的技术，调整转移概率，将“selfreturning”边缘的归一化。为了评估其有效性的经验，两个检索方法使用RWWR的搜索性能进行了比较，使用标准的RWR的方法，性能测量的平均精度和nDCG。实验是在PubMed Central的开放获取子集中创建的测试集合上进行的，结果表明RWWR方法往往优于标准RWR方法。
One of the latest algorithms for computing similarities between nodes in a graph is Random Walk with Restart (RWR). However, on a document co-citation network for similar document search, computing transition probabilities remains difficult. To solve the problem, this paper proposes a Random Walk with Wait and Restart (RWWR) algorithm, which contains a new technique for adjusting the transition probability by incorporating a “selfreturning” edge into the normalization. To evaluate its effectiveness empirically, the search performance of two retrieval methods using RWWR was compared to a method using the standard RWR; the performance was measured by average precision and nDCG. The experiment was conducted on a test collection created from the Open Access Subset of PubMed Central, and the results indicated that the RWWR methods tend to outperform the standard RWR method.