Panther：大型网络中的快速Top-k相似性搜索
Panther: Fast Top-k Similarity Search in Large Networks
摘要：
在诸如社交网络和生物网络的各种领域中，估计顶点之间的相似性是网络分析中的基本问题。基于公共邻居和结构上下文的方法受到了广泛的关注。然而，这两类方法都很难扩展到处理大型网络（具有数十亿个节点）。在本文中，我们提出了一种抽样方法，可证明和准确地估计顶点之间的相似性。该算法基于一种新的随机路径的思想，并提出了一种扩展的方法，以增强两个顶点完全不连通时的结构相似性。我们提供了理论证明的误差界和置信度的算法。我们进行了广泛的实证研究，并表明我们的算法可以获得网络中任何顶点的top-k相似顶点，速度比最先进的方法快约300倍。我们还使用身份解析和结构洞发现，社交网络中的两个重要应用，以评估估计的相似性的准确性。我们的实验结果表明，该算法取得了明显优于其他几种方法的性能。
英文摘要：
Estimating similarity between vertices is a fundamental issue in network analysis across various domains, such as social networks and biological networks. Methods based on common neighbors and structural contexts have received much attention. However, both categories of methods are difficult to scale up to handle large networks (with billions of nodes). In this paper, we propose a sampling method that provably and accurately estimates the similarity between vertices. The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected. We provide theoretical proofs for the error-bound and confidence of the proposed algorithm. We perform extensive empirical study and show that our algorithm can obtain top-k similar vertices for any vertex in a network approximately 300x faster than state-of-the-art methods. We also use identity resolution and structural hole spanner finding, two important applications in social networks, to evaluate the accuracy of the estimated similarities. Our experimental results demonstrate that the proposed algorithm achieves clearly better performance than several alternative methods.
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