Efficient Skyline Community Discovery in Large Networks
Date
2022-08-30
Authors
Akber, Mohammad Ali
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Abstract
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.
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Keywords
Skyline, Community Detection, Skyline Community, Quad Tree, Trie, Spacial Indexing