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Community classification on Decentralized Social Networks based on 2-hop neighbourhood information

机译：基于2跳邻里信息的分散社交网络的社区分类

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Decentralized Social Network (DSN) has attracted a lot of research and development interest in recent years. It is believed to be the solution to many problems of centralized services. Due to the data limitation imposed by common decentralized architectures, centralized algorithms that support social networking functions need to be re-designed. In this work, we tackle the problem of community detection for a given user under the constraint of limited local topology information. This naturally yields a classification formulation for community detection. As an initial study, we focus on a specific type of classifiers - classification by thresholding against a proximity measure between nodes. We investigated four proximity measures: Common Neighbours (CN), Adamic/Adar score (AA), Page Rank (PR), Personalized PageRank (PPR). Using data collected from a large-scale Social Networking Service (SNS) in practice, we show that PPR can outperform the others with a few pre-known labels (37.5% to 64.97% relative improvement in terms of Area Under the ROC Curve). We further carry out extensive numerical evaluation of PPR, showing that more pre-known labels can linearly increase the capability of the single-feature classifier based on PPR. Users can thus seek for a trade-off between labeling cost and classification accuracy.
机译：分散的社交网络（DSN）近年来吸引了大量的研发利益。它被认为是对集中服务许多问题的解决方案。由于共同的分散架构施加的数据限制，需要重新设计支持社交网络功能的集中算法。在这项工作中，我们在限制当地拓扑信息的约束下解决了给定用户的社区检测问题。这自然产生了社区检测的分类制剂。作为初步研究，我们专注于特定类型的分类器 - 通过抵御节点之间的接近度量来分类。我们调查了四个接近措施：常见的邻居（CN），善/ ADAR得分（AA），页面排名（PR），个性化PageRank（PPR）。使用从大规模社交网络服务（SNS）中收集的数据在实践中，我们表明PPR可以在少数人的预先已知的标签（37.5％至64.97％的ROC曲线下的区域的相对改善）优于其他人。我们进一步对PPR进行了广泛的数值评估，表明更多的预先已知的标签可以基于PPR线性地提高单个特征分类器的能力。因此，用户可以在标签成本和分类准确性之间寻求权衡。

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