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Efficient Skyline Community Discovery in Large NetworksAkber, Mohammad Ali 30 August 2022 (has links)
Every entity in the real world can be described uniquely by it’s attributes. It is possible to rank similar entities based on these attributes, i.e. a professor can be ranked by his/her number of publications, citations etc. A community is formed by a group of connected entities. Individual ranking of an entity plays an important role in the quality of a community. Skyline community in a network represents the highest ranked communities in the network. But how do we define this ranking? Ranking system in some model considers only a single attribute [16], whereas the other [15] [23] considers multiple attributes. Intuitively multiple attributes represent a community better and produce good results. We propose a novel community discovery model, which considers multiple attribute when ranking the community and is efficient in terms of computation time and result size. We use a progressive (can produce re- sults gradually without depending on the future processing) algorithm to calculate the community in an order such that a community is guaranteed not to be dominated by those generated after it. And to verify the dominance relationship between two communities, we came up with a range based comparison where the dominance rela- tionship is decided by the set of nodes each group dominates. If domination list of a group is a subset of another group, we say the second group dominates the first. Because a groups domination list contains it’s member along with the nodes they dominate. So in the example, the second group dominates every node of the first group. / Graduate
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