期刊
NATURE COMMUNICATIONS
卷 7, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/ncomms11863
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资金
- National Institute of Child Health and Human Development [P01HD31921]
- US National Science Foundation [DMS-1107796, DMS-1407207, IIS-1452718]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1452718] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1407207, 1107796] Funding Source: National Science Foundation
For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this 'metadata' can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains.
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