我朋友刚分享了个新方法，能在概率空间给多变量数据画图，和之前方法比更通用，不管数据啥样都能用呢。
该研究提出一种在概率度量空间内学习随机几何图（RGG）的方法，适用于多变量数据集。该方法通过定义新的距离函数构建图结构，该函数基于节点间连接性的概率分布。实验证明此学习方法能处理任意大小的数据，且度分布与观测变量间的相关性相关。
Learning Random Geometric Graphs Drawn in Probabilistic Metric Spaces
We present a new data-driven learning of a Random Geometric Graph (RGG) of a multivariate dataset, where the graph is drawn in a probabilistic metric space. This graph learning works for generic datasets, irrespective of the type of the observables; their probability distributions; or size of the data. We identify a metric of the space that the graph is drawn in, as a probability distribution of a random variable that we introduce, namely, a variable that represents the disparity between the connectedness of two vertices of the graph, and the correlation between the two random variables that are attached to the respective vertex. It is the closed-form {\it{cdf}} of this disparity variable that we advance as the distance function of the host space of the learnt RGG, such that the edge exists between any two nodes, if this inter-nodal distance falls short of a chosen cutoff probability. Drawing the RGG in this probabilistic space leads to the graph being an Soft RGG, such that any edge - if it exists - exists with an identified probability. We forward a simple Rejection Sampling-based technique for learning the probability of any edge. The expected degree distribution of a vertex of this RGG is identified as local, and dependent on the inter-observable correlation matrix. If said correlation matrix is not known, it can be learnt given the data, using its closed-form posterior probability density function, that we forward. We illustrate our graph learning method by learning multiple RGGs of highly multivariate real datasets.