Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we
study a version of constrained SC in which we try to incorporate the fairness notion …

Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for
the important task of node clustering has not been fully exploited. In particular, most state-of …

We introduce a principled and theoretically sound spectral method for k-way clustering in
signed graphs, where the affinity measure between nodes takes either positive or negative …

The recent integration of deep learning and pairwise similarity annotation-based
constrained clustering—ie, deep constrained clustering (DCC)—has proven effective for …

We consider the classical problem of establishing a statistical ranking of a set of n items
given a set of inconsistent and incomplete pairwise comparisons between such items …

Signed networks are graphs where edges are annotated with a positive or negative sign,
indicating whether an edge interaction is friendly or antagonistic. Signed networks can be …

State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs
multiple levels of progressively coarser hypergraphs that are used to drive cut refinements …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

Empirical networks often exhibit different meso-scale structures, such as community and
core–periphery structures. Core–periphery structure typically consists of a well-connected …