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Complete graph reconstruction from partial information

dc.contributor.authorMahindre, Gunjan S., author
dc.contributor.authorJayasumana, Anura P., author
dc.date.accessioned2017-11-10T13:48:32Z
dc.date.available2017-11-10T13:48:32Z
dc.date.issued2017
dc.description.abstractNetworks, biological molecules, neural structures can be represented as graphs. Data processing and storage of such structures with millions of nodes is very bulky. Thus, we derive important properties and synthesize a technique to regenerate a graph from partial information about the graph with minimum data and high fidelity. This will impact the way we store and operate on network data and opens new possibilities in areas such as chemistry, social networks, neural networks and the ever evolving Internet.en_US
dc.format.mediumborn digital
dc.format.mediumStudent works
dc.format.mediumposters
dc.identifier.urihttps://hdl.handle.net/10217/184811
dc.languageEnglishen_US
dc.language.isoengen_US
dc.publisherColorado State University. Librariesen_US
dc.relation.ispartof2017 Projects
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectreconstruction metric dimension
dc.subjectsmall world networks
dc.subjectgraph mining
dc.titleComplete graph reconstruction from partial informationen_US
dc.title.alternative174 - Gunjan Shrikrishna Mahindreen_US
dc.title.alternativeComplete reconstruction of graphs from partial dataen_US
dc.title.alternativeReconstruction of large scale networks from partial informationen_US
dc.typeTexten_US
dc.typeImageen_US
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