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- W3135416636 abstract "Abstract Finding communities that are not only relatively densely connected in a graph but that also show similar characteristics based on attribute information has drawn strong attention in the last years. There exists already a remarkable body of work that attempts to find communities in vertex-attributed graphs that are relatively homogeneous with respect to attribute values. Yet, it is scattered through different research fields and most of those publications fail to make the connection. In this paper, we identify important characteristics of the different approaches and place them into three broad categories: those that select descriptive attributes , related to clustering approaches, those that enumerate attribute-value combinations , related to pattern mining techniques, and those that identify conditional attribute weights, allowing for post-processing. We point out that the large majority of these techniques treat the same problem in terms of attribute representation, and are therefore interchangeable to a certain degree. In addition, different authors have found very similar algorithmic solutions to their respective problem." @default.
- W3135416636 created "2021-03-15" @default.
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- W3135416636 date "2021-02-27" @default.
- W3135416636 modified "2023-09-25" @default.
- W3135416636 title "Mining communities and their descriptions on attributed graphs: a survey" @default.
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- W3135416636 doi "https://doi.org/10.1007/s10618-021-00741-z" @default.
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