论文标题
建模网络中社区检测的节点暴露
Modeling Node Exposure for Community Detection in Networks
论文作者
论文摘要
在社区检测中，数据集经常会遭受抽样偏见，而该偏差通常会具有高亲和力的节点似乎具有零亲和力。例如，当社交网络的两个仿射用户没有彼此接触时，就会发生这种情况。当将仿射节点视为非仿射时，对这种数据的社区发现会受到损害。为了解决这个问题，我们通过引入一组其他隐藏变量来明确对贝叶斯社区检测框架中的（非）暴露机制进行建模。与未建模暴露的方法相比，我们的方法能够更好地重建输入图，同时在恢复社区中保持相似的性能。重要的是，它允许估算暴露两个节点的概率，标准模型不可用。
In community detection, datasets often suffer a sampling bias for which nodes which would normally have a high affinity appear to have zero affinity. This happens for example when two affine users of a social network were not exposed to one another. Community detection on this kind of data suffers then from considering affine nodes as not affine. To solve this problem, we explicitly model the (non-)exposure mechanism in a Bayesian community detection framework, by introducing a set of additional hidden variables. Compared to approaches which do not model exposure, our method is able to better reconstruct the input graph, while maintaining a similar performance in recovering communities. Importantly, it allows to estimate the probability that two nodes have been exposed, a possibility not available with standard models.
