论文标题
重要的关系：从在线社交网络中的相似性度量的角度来看
The Ties that matter: From the perspective of Similarity Measure in Online Social Networks
论文作者
论文摘要
在线社交网络已经着手进行连接强度度量的重要性，该措施具有广泛的应用程序，例如分析扩散行为，社区检测，链接预测，推荐系统。尽管存在一些现有的连接强度度量，但连接与邻居共享的密度以及方向性方面的关注并没有得到太多关注。在本文中，我们提出了一种不对称的边缘相似性度量，即基于邻域密度的边缘相似性（NDE），它提供了基本的支持以得出连接强度。 NDE的时间复杂性为$ O（NK^2）$。显示了NDE在社交网络中进行社区检测的应用。我们考虑了基于相似性的社区检测技术，并用NDE代替了其相似性度量。根据检测社区的有效性，在几个小型现实世界数据集上评估了NDE的性能，并将其与三种广泛使用的相似性度量进行了比较。经验结果表明，NDE可以在准确性和质量方面检测到相对更好的社区。
Online Social Networks have embarked on the importance of connection strength measures which has a broad array of applications such as, analyzing diffusion behaviors, community detection, link predictions, recommender systems. Though there are some existing connection strength measures, the density that a connection shares with it's neighbors and the directionality aspect has not received much attention. In this paper, we have proposed an asymmetric edge similarity measure namely, Neighborhood Density-based Edge Similarity (NDES) which provides a fundamental support to derive the strength of connection. The time complexity of NDES is $O(nk^2)$. An application of NDES for community detection in social network is shown. We have considered a similarity based community detection technique and substituted its similarity measure with NDES. The performance of NDES is evaluated on several small real-world datasets in terms of the effectiveness in detecting communities and compared with three widely used similarity measures. Empirical results show NDES enables detecting comparatively better communities both in terms of accuracy and quality.
