Core-periphery identification in massive networks

Abstract

Modern networks can be huge with millions or even billions of nodes and edges. Thus, algorithms must be capable of scaling to such large networks in order to be practically useful. In this work, we are interested in developing an algorithm to identify core-periphery structure in massive networks. Core-periphery structure is a meso-scale feature where nodes are grouped into a densely connected core or sparsely connected periphery. To identify such structures in large networks, we propose a divide-and-conquer algorithm. The key feature of our algorithm is leveraging the edge list representation of the network, instead of the adjacency matrix, as it tends to be faster and makes a more efficient use of memory. We apply the proposed algorithm to synthetic and real-world data, notably demonstrating its performance on a real-world network with almost 14 million edges without loading the entire network into memory.

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BibTeX RIS

BibTeX

@misc{yanchenko2026coreperiphery,
  title = {{Core-periphery identification in massive networks}},
  author = {Eric Yanchenko},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.00008},
  eprint = {2609.00008},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.00008},
}

RIS

TY  - UNPB
TI  - Core-periphery identification in massive networks
AU  - Eric Yanchenko
PY  - 2026
DA  - 2026/09/01
PB  - arXiv
DO  - 10.48550/arxiv.2609.00008
UR  - https://arxiv.org/abs/2609.00008
AB  - Modern networks can be huge with millions or even billions of nodes and edges. Thus, algorithms must be capable of scaling to such large networks in order to be practically useful. In this work, we are interested in developing an algorithm to identify core-periphery structure in massive networks. Core-periphery structure is a meso-scale feature where nodes are grouped into a densely connected core or sparsely connected periphery. To identify such structures in large networks, we propose a divide-and-conquer algorithm. The key feature of our algorithm is leveraging the edge list representation of the network, instead of the adjacency matrix, as it tends to be faster and makes a more efficient use of memory. We apply the proposed algorithm to synthetic and real-world data, notably demonstrating its performance on a real-world network with almost 14 million edges without loading the entire network into memory.
ER  -

Abstract

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