Hypergraphs are important for processing data with higher-order relationships involving
more than two entities. In scenarios where explicit hypergraphs are not readily available, it is …

Multi-paradigm deep learning models show great potential for dynamic functional
connectivity (dFC) analysis by integrating complementary information. However, many of …

Hypergraph structure learning, which aims to learn the hypergraph structures from the
observed signals to capture the intrinsic high-order relationships among the entities …

The performance of most clustering methods hinges on the used pairwise affinity, which is
usually denoted by a similarity matrix. However, the pairwise similarity is notoriously known …

Tensor spectral clustering (TSC) is an emerging approach that explores multi-wise
similarities to boost learning. However, two key challenges have yet to be well addressed in …

Exploring the hierarchical structure of graphs presents notable advantages for graph
analysis, revealing insights ranging from individual vertex behavior to community distribution …

Traditional clustering methods rely on pairwise affinity to divide samples into different
subgroups. However, high-dimensional small-sample (HDLSS) data are affected by the …

Predicting drug–drug interactions (DDIs) is the problem of predicting side effects (unwanted
outcomes) of a pair of drugs using drug information and known side effects of many pairs …

The inference of reliable and meaningful connectivity information from weights representing
the affinity between nodes in a graph is an outstanding problem in network science. Usually …

Numerous biological experiments have demonstrated that microRNA (miRNA) is involved in
gene regulation within cells, and mutations and abnormal expression of miRNA can cause a …