The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

This study develops a framework for a class of constant modulus (CM) optimization
problems, which covers binary constraints, discrete phase constraints, semi-orthogonal …

We introduce the triangle-densest-K-subgraph problem (TDKS) for undirected graphs: given
a size parameter K, compute a subset of K vertices that maximizes the number of induced …

Centrality measures, quantifying the importance of vertices or edges, play a fundamental
role in network analysis. To date, triggered by some positive approximability results, a large …

In the first part of this study, a convex-constrained penalized formulation was studied for a
class of constant modulus (CM) problems. In particular, the error bound techniques were …

Dense subgraph discovery (DSD) is a key primitive in graph mining that typically deals with
extracting cliques and near-cliques. In this paper, we revisit the optimal quasi-clique (OQC) …

We introduce the triangle-densest-k-subgraph problem (TDkS) for undirected graphs: given
a size parameter k, compute a subset of k vertices that maximizes the number of induced …

Cardinality-constrained binary quadratic optimization appears in various applications such
as finding a densest size-constrained subgraph from a graph. It is a challenging …

Search mechanisms over large assortments of items are central to the operation of many
platforms. As users commonly express filtering conditions based on item properties that are …

Dense subgraph discovery (DSD) is a key graph mining primitive with myriad applications
including finding densely connected communities which are diverse in their vertex …