  Identifying cohesive subgraphs in hypergraphs is a fundamental problem that
has received recent attention in data mining and engineering fields. Existing
approaches mainly focus on a strongly induced subhypergraph or edge
cardinality, overlooking the importance of the frequency of co-occurrence. In
this paper, we propose a new cohesive subgraph named (k,g)-core, which
considers both neighbour and co-occurrence simultaneously. The $(k,g)$-core has
various applications including recommendation system, network analysis, and
fraud detection. To the best of our knowledge, this is the first work to
combine these factors. We extend an existing efficient algorithm to find
solutions for $(k,g)$-core. Finally, we conduct extensive experimental studies
that demonstrate the efficiency and effectiveness of our proposed algorithm.
