Sifting out communities in large sparse networks

arXiv:2405.00816v1 Announce Type: cross
Abstract: Research data sets are growing to unprecedented sizes and network modeling is commonly used to extract complex relationships in diverse domains, such as genetic interactions involved in disease, logistics, and social communities. As the number of nodes increases in a network, an increasing sparsity of edges is a practical limitation due to memory restrictions. Moreover, many of these sparse networks exhibit very large numbers of nodes with no adjacent edges, as well as disjoint components …

abstract arxiv communities cs.lg cs.si data data sets disease diverse domains extract interactions logistics modeling network networks nodes relationships research social sparsity type