English

Distance-generalized Core Decomposition

Data Structures and Algorithms 2019-04-17 v1 Social and Information Networks

Abstract

The kk-core of a graph is defined as the maximal subgraph in which every vertex is connected to at least kk other vertices within that subgraph. In this work we introduce a distance-based generalization of the notion of kk-core, which we refer to as the (k,h)(k,h)-core, i.e., the maximal subgraph in which every vertex has at least kk other vertices at distance h\leq h within that subgraph. We study the properties of the (k,h)(k,h)-core showing that it preserves many of the nice features of the classic core decomposition (e.g., its connection with the notion of distance-generalized chromatic number) and it preserves its usefulness to speed-up or approximate distance-generalized notions of dense structures, such as hh-club. Computing the distance-generalized core decomposition over large networks is intrinsically complex. However, by exploiting clever upper and lower bounds we can partition the computation in a set of totally independent subcomputations, opening the door to top-down exploration and to multithreading, and thus achieving an efficient algorithm.

Keywords

Cite

@article{arxiv.1904.07262,
  title  = {Distance-generalized Core Decomposition},
  author = {Francesco Bonchi and Arijit Khan and Lorenzo Severini},
  journal= {arXiv preprint arXiv:1904.07262},
  year   = {2019}
}
R2 v1 2026-06-23T08:40:18.796Z