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嵌入Wasserstein空间的深变分网络

Deep Variational Network Embedding in Wasserstein Space
课程网址: http://videolectures.net/kdd2018_zhu_wasserstein_space/  
主讲教师: Dingyuan Zhu
开课单位: 清华大学
开课时间: 2018-11-23
课程语种: 英语
中文简介:
网络嵌入旨在将网络嵌入到低维向量空间中,同时保持网络固有的结构财产,近年来受到了广泛关注。现有的大多数嵌入方法都将节点作为点向量嵌入到低维连续空间中。这样,边缘的形成是确定性的,并且仅由节点的位置决定。然而,现实世界网络的形成和演化充满了不确定性,这使得这些方法不是最优的。为了解决这个问题,本文提出了一种新的嵌入在Wasserstein空间中的深度变分网络(DVNE)。该方法学习Wasserstein空间中的高斯分布作为每个节点的潜在表示,可以同时保持网络结构并对节点的不确定性进行建模。具体而言,我们使用2-Wasserstein距离作为分布之间的相似性度量,这可以很好地保持网络中的传递性,且具有线性计算成本。此外,我们的方法通过深度变分模型暗示了均值和方差的数学相关性,它可以通过均值向量很好地捕捉节点的位置,通过方差很好地捕获节点的不确定性。此外,我们的方法通过保持网络中的一阶和二阶接近度来捕获局部和全局网络结构。我们的实验结果表明,与最先进的方法相比,我们的方法可以有效地对网络中节点的不确定性进行建模,并在链路预测和多标签分类等实际应用中显示出显著的优势。
课程简介: Network embedding, aiming to embed a network into a low dimensional vector space while preserving the inherent structural properties of the network, has attracted considerable attentions recently. Most of the existing embedding methods embed nodes as point vectors in a low-dimensional continuous space. In this way, the formation of the edge is deterministic and only determined by the positions of the nodes. However, the formation and evolution of real-world networks are full of uncertainties, which makes these methods not optimal. To address the problem, we propose a novel Deep Variational Network Embedding in Wasserstein Space (DVNE) in this paper. The proposed method learns a Gaussian distribution in the Wasserstein space as the latent representation of each node, which can simultaneously preserve the network structure and model the uncertainty of nodes. Specifically, we use 2-Wasserstein distance as the similarity measure between the distributions, which can well preserve the transitivity in the network with a linear computational cost. Moreover, our method implies the mathematical relevance of mean and variance by the deep variational model, which can well capture the position of the node by the mean vectors and the uncertainties of nodes by the variance. Additionally, our method captures both the local and global network structure by preserving the first-order and second-order proximity in the network. Our experimental results demonstrate that our method can effectively model the uncertainty of nodes in networks, and show a substantial gain on real-world applications such as link prediction and multi-label classification compared with the state-of-the-art methods.
关 键 词: 网络嵌入; 向量空间; 链路预测
课程来源: 视频讲座网
数据采集: 2023-03-04:chenjy
最后编审: 2023-05-11:chenjy
阅读次数: 32