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How should we gather information in a network, where each node's visibility is limited to its local neighborhood? This problem arises in numerous real-world applications, such as surveying and task routing in social networks, team formation in collaborative networks and experimental design with dependency constraints. Often the informativeness of a set of nodes can be quantified via a submodular utility function. Existing approaches for submodular optimization, however, require that the set of all nodes that can be selected is known ahead of time, which is often unrealistic. In contrast, we propose a novel model where we start our exploration from an initial node, and new nodes become visible and available for selection only once one of their neighbors has been chosen. We then present a general algorithm NetExp for this problem, and provide theoretical bounds on its performance dependent on structural properties of the underlying network. We evaluate our methodology on various simulated problem instances as well as on data collected from social question answering system deployed within a large enterprise.
Information Gathering in Networks via Active Exploration A. Singla, E. Horvitz, P. Kohli, R. White, A. KrauseIn International Joint Conference on Artificial Intelligence (IJCAI), 2015
Bibtex Entry:
	author = {Adish Singla and Eric Horvitz and Pushmeet Kohli and Ryen White and Andreas Krause},
	booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
	month = {July},
	title = {Information Gathering in Networks via Active Exploration},
	year = {2015}}