具有相互偏好近邻的关系主题聚类
Relational Thematic Clustering with Mutually Preferred Neighbors
摘要：
自动学习网络数据中的主题簇一直是机器学习领域的一个挑战性课题。已经提出了许多方法来实现它，利用边缘，顶点特征，或两者兼而有之。然而，很少有人考虑如何量化的二分法倾向w.r.t.网络拓扑和顶点特征可能影响顶点簇偏好，这阻碍了先前的方法在网络数据中发现更多可解释的潜在组。为了填补这一空白，我们提出了一种新的概率模型，被称为关系主题聚类与相互偏好的邻居（RTCMPN）。与现有的边结构和顶点特征的学习重要性预先确定的方法不同，RTCMPN可以进一步学习潜在的偏好，指示哪些相邻顶点更可能在同一个簇中，以及描述相对重要性如何w.r.t.边结构和顶点特征可以影响成对顶点之间的关联。因此，植入边缘结构，顶点特征，邻居的喜好，和顶点-顶点二分倾向的集群结构可以通过RTCMPN学习。我们还推导出一个有效的期望最大化算法RTCMPN推断最优模型参数。RTCMPN与各种网络数据的几个强基线进行了比较。实验结果验证了RTCMPN的有效性。
英文摘要：
Automatically learning thematic clusters in network data has long been a challenging task in machine learning community. A number of approaches have been proposed to accomplish it, utilizing edges, vertex features, or both aforementioned. However, few of them consider how the quantification of dichotomous inclination w.r.t. network topology and vertex features may influence vertex-cluster preferences, which deters previous methods from uncovering more interpretable latent groups in network data. To fill this void, we propose a novel probabilistic model, dubbed Relational Thematic Clustering with Mutually Preferred Neighbors (RTCMPN). Different from prevalent approaches which predetermine the learning significance of edge structure and vertex features, RTCMPN can further learn the latent preferences indicating which neighboring vertices are more possible to be in the same cluster, and the dichotomous inclinations describing how relative significance w.r.t. edge structure and vertex features may impact the association between pairwise vertices. Therefore, cluster structure implanted with edge structure, vertex features, neighboring preferences, and vertex-vertex dichotomous inclinations can be learned by RTCMPN. We additionally derive an effective Expectation-Maximization algorithm for RTCMPN to infer the optimal model parameters. RTCMPN has been compared with several strong baselines on various network data. The remarkable results validate the effectiveness of RTCMPN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
