document: DivRank : the interplay of prestige and diversity in information networks Information networks are widely used to characterize the relationships between data items such as text documents . Many important retrieval and mining tasks rely on ranking the data items based on their centrality or prestige in the network . Beyond prestige , diversity has been recognized as a crucial objective in ranking , aiming at providing a non-redundant and high coverage piece of information in the top ranked results . Nevertheless , existing network-based ranking approaches either disregard the concern of diversity , or handle it with non-optimized heuristics , usually based on greedy vertex selection . We propose a novel ranking algorithm , DivRank , based on a reinforced random walk in an information network . This model automatically balances the prestige and the diversity of the top ranked vertices in a principled way . DivRank not only has a clear optimization explanation , but also well connects to classical models in mathematics and network science . We evaluate DivRank using empirical experiments on three different networks as well as a text summarization task . DivRank outperforms existing network-based ranking methods in terms of enhancing diversity in prestige .
doc_bio_tags: O O O O O O O B O B I B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O B I I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O