Ningchen Ying

Ningchen Ying, Tsz Wai Wu

In this paper, we introduce Super Luminal ("xLumi"), a new payment channel protocol for blockchain systems. xLumi is a simple unidirectional payment channel that can be extended to a bidirectional payment channel or a complete network. xLumi guarantees the security of the payment channel's funds by using a simple set of mathematical rules that can be easily implemented on any blockchain with the necessary infrastructure. We also give the detailed implementation methods of this idea using V Systems contract systems in this paper.

Ting Li, Ningchen Ying, Xianshi Yu et al.

Community detection was a hot topic on network analysis, where the main aim is to perform unsupervised learning or clustering in networks. Recently, semi-supervised learning has received increasing attention among researchers. In this paper, we propose a new algorithm, called weighted inverse Laplacian (WIL), for predicting labels in partially labeled networks. The idea comes from the first hitting time in random walk, and it also has nice explanations both in information propagation and the regularization framework. We propose a partially labeled degree-corrected block model (pDCBM) to describe the generation of partially labeled networks. We show that WIL ensures the misclassification rate is of order $O(\frac{1}{d})$ for the pDCBM with average degree $d=Ω(\log n),$ and that it can handle situations with greater unbalanced than traditional Laplacian methods. WIL outperforms other state-of-the-art methods in most of our simulations and real datasets, especially in unbalanced networks and heterogeneous networks.

Ting Li, Bingyi Jing, Ningchen Ying et al.

Preprocessing data is an important step before any data analysis. In this paper, we focus on one particular aspect, namely scaling or normalization. We analyze various scaling methods in common use and study their effects on different statistical learning models. We will propose a new two-stage scaling method. First, we use some training data to fit linear regression model and then scale the whole data based on the coefficients of regression. Simulations are conducted to illustrate the advantages of our new scaling method. Some real data analysis will also be given.