用邻域平滑估计网络边缘概率

Revisiting Over-smoothing in Deep GCNs

Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs). In this paper, we propose a new view that deep GCNs can actually learn to anti-oversmooth during training. This work interprets a standard GCN architecture as layerwise integration of a Multi-layer Perceptron (MLP) and graph regularization. We analyze and conclude that before training, the final representation of a deep GCN does over-smooth, however, it learns anti-oversmoothing during training. Based on the conclusion, the paper further designs a cheap but effective trick to i…

Calibrated neighborhood aware confidence measure for deep metric learning

Deep metric learning has gained promising improvement in recent years following the success of deep learning. It has been successfully applied to problems in few-shot learning, image retrieval, and open-set classifications. However, measuring the confidence of a deep metric learning model and identifying unreliable predictions is still an open challenge. This paper focuses on defining a calibrated and interpretable confidence metric that closely reflects its classification accuracy. While performing similarity comparison directly in the latent space using the learned distance metric, our appro…

Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization

Graph convolutional neural networks (GCNNs) have received much attention recently, owing to their capability in handling graph-structured data. Among the existing GCNNs, many methods can be viewed as instances of a neural message passing motif; features of nodes are passed around their neighbors, aggregated and transformed to produce better nodes' representations. Nevertheless, these methods seldom use node transition probabilities, a measure that has been found useful in exploring graphs. Furthermore, when the transition probabilities are used, their transition direction is often improperly c…

Signal Processing and Piecewise Convex Estimation

Many problems on signal processing reduce to nonparametric function estimation. We propose a new methodology, piecewise convex fitting (PCF), and give a two-stage adaptive estimate. In the first stage, the number and location of the change points is estimated using strong smoothing. In the second stage, a constrained smoothing spline fit is performed with the smoothing level chosen to minimize the MSE. The imposed constraint is that a single change point occurs in a region about each empirical change point of the first-stage estimate. This constraint is equivalent to requiring that the third d…

Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks

In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph are interconnected. The Graph Neural Network (GNN) has proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying gr…

VisitHGNN：用于兴趣点到访模式建模的异构图神经网络

VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns

本研究提出异构图神经网络VisitHGNN，融合多源属性与空间等关联预测POI到访概率，在富尔顿县数据上表现优于基线，可为城市相关领域决策提供支撑。

关于使用Bagging进行局部内在维数估计的使用

On the Use of Bagging for Local Intrinsic Dimensionality Estimation

本文提出使用Bagging方法减少局部内在维数估计中的方差，通过子Bagging保留最近邻距离的局部分布，分析采样率和k-NN大小对性能的影响，实验表明Bagging能有效降低方差和均方误差。