用于网络建模的稀疏矩阵变量高斯过程块模型
Sparse matrix-variate Gaussian process blockmodels for network modeling
摘要：
我们面对来自各种来源的网络数据，例如蛋白质相互作用和在线社交网络。一个关键问题是对网络交互进行建模，并识别潜在的网络节点组。这个问题是具有挑战性的，由于许多原因。例如，网络节点是相互依赖的而不是彼此独立的，并且已知数据是非常嘈杂的（例如，缺失边缘）。为了解决这些问题，我们提出了一种新的网络数据关系模型，稀疏矩阵变量高斯过程块模型（SMGB）。我们的模型概括了流行的双线性生成模型，并使用具有潜在成员变量的矩阵变量高斯过程捕获非线性网络交互。我们还分配稀疏先验分布的潜在成员变量学习稀疏组分配的个别网络节点。为了有效地从数据中估计潜变量，我们开发了一种有效的变分期望最大化方法。我们将我们的方法与合成和真实世界网络数据集上的几种最先进的网络模型进行了比较。实验结果表明，SMGB优于其他方法在发现潜在类或预测未知的相互作用。
英文摘要：
We face network data from various sources, such as protein interactions and online social networks. A critical problem is to model network interactions and identify latent groups of network nodes. This problem is challenging due to many reasons. For example, the network nodes are interdependent instead of independent of each other, and the data are known to be very noisy (e.g., missing edges). To address these challenges, we propose a new relational model for network data, Sparse Matrix-variate Gaussian process Blockmodel (SMGB). Our model generalizes popular bilinear generative models and captures nonlinear network interactions using a matrix-variate Gaussian process with latent membership variables. We also assign sparse prior distributions on the latent membership variables to learn sparse group assignments for individual network nodes. To estimate the latent variables efficiently from data, we develop an efficient variational expectation maximization method. We compared our approaches with several state-of-the-art network models on both synthetic and real-world network datasets. Experimental results demonstrate SMGBs outperform the alternative approaches in terms of discovering latent classes or predicting unknown interactions.
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。
