Structure and inference in annotated networks

• Published in: Nature communications 2016-06, Vol.7 (1), p.11863-11863, Article 11863
• Main Authors: Newman, M. E. J., Clauset, Aaron
• Format: Article
• Language: English
• Subjects: 631/553/1833; Age; Algorithms; Aquatic Organisms - physiology; Benchmarking; Community Networks - statistics & numerical data; Computer science; Datasets as Topic; Erythrocytes - parasitology; Female; Food Chain; Gender; Gene Expression Regulation; Humanities and Social Sciences; Humans; Male; Metabolism; Metadata; Methods; multidisciplinary; Plasmodium falciparum - genetics; Plasmodium falciparum - metabolism; Protozoan Proteins - genetics; Protozoan Proteins - metabolism; Recombination, Genetic; Science; Science (multidisciplinary); Social Networking; Social networks; Statistical inference; Students - psychology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms11863

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. Analysis of network structure is usually based on knowledge of connections alone, ignoring additional information such as gender or age of individuals in social networks. Here the authors devise an approach that incorporates such metadata and uses it to improve the detection of network communities.