A hypergraph is a generalization of a graph that arises naturally when attribute-sharing among entities is considered. Compared to graphs, hypergraphs have the distinct advantage that they contain explicit communities and are more convenient to manipulate. An open problem in hypergraph research is how to accurately and efficiently calculate node distances on hypergraphs.
View Article and Find Full Text PDFResearchers have designed many algorithms to measure the distances between graph nodes, such as average hitting times of random walks, cosine distances from DeepWalk, personalized PageRank, etc. Successful though these algorithms are, still they are either underperforming or too time consuming to be applicable to huge graphs that we encounter daily in this big data era. To address these issues, here we propose a faster algorithm based on an improved version of random walks that can beat DeepWalk results with more than 10 times acceleration.
View Article and Find Full Text PDFObjective: To compare the severity of psychological distress between patients with epilepsy and healthy controls during the COVID-19 outbreak in southwest China, as well as identify potential risk factors of severe psychological distress among patients with epilepsy.
Methods: This cross-sectional case-control study examined a consecutive sample of patients older than 15 years treated at the epilepsy center of West China Hospital between February 1 and February 29, 2020. As controls, sex- and age-matched healthy visitors of inpatients (unrelated to the patients) were also enrolled during the same period.