Mahindre, Gunjan S., authorJayasumana, Anura P., author2017-11-102017-11-102017https://hdl.handle.net/10217/184811Networks, 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.born digitalStudent workspostersengCopyright 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.reconstruction metric dimensionsmall world networksgraph miningComplete graph reconstruction from partial information174 - Gunjan Shrikrishna MahindreComplete reconstruction of graphs from partial dataReconstruction of large scale networks from partial informationText