document: Learning to rank networked entities Several algorithms have been proposed to learn to rank entities modeled as feature vectors , based on relevance feedback . However , these algorithms do not model network connections or relations between entities . Meanwhile , Pagerank and variants find the stationary distribution of a reasonable but arbitrary Markov walk over a network , but do not learn from relevance feedback . We present a framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges . We propose two flavors of conductance learning problems in our framework . In the first setting , relevance feedback comparing node-pairs hints that the user has one or more hidden preferred communities with large edge conductance , and the algorithm must discover these communities . We present a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer . In the second setting , edges have types , and relevance feedback hints that each edge type has a potentially different conductance , but this is fixed across the whole network . Our algorithm learns the conductances using an approximate Newton method .
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