Titlebook: Advances in Social Network Mining and Analysis; Second International Lee Giles,Marc Smith,Haizheng Zhang Conference proceedings 2010 The Ed

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https://doi.org/10.1007/978-3-030-13712-0 community detection. Existing algorithms, including the popular modularity-optimization methods, look for regions of the network that are better connected internally, e.g., have higher than expected number of edges within them. We believe, however, that edges do not give the true measure of network

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Recognizing Chords with EDS: Part One,n the context of the spread of a disease or undesirable behavior, it is important to identify .: individuals that are most effective in stopping or slowing down the spread of a process through the population. This problem has so far resisted systematic algorithmic solutions. In an effort to formulat

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Mika Kuuskankare,Mikael Laurson work studies them in isolation. Here, we investigate how these networks can be overlaid, and propose a feature taxonomy for link prediction. We show that when there are tightly-knit family circles in a social network, we can improve the accuracy of link prediction models. This is done by making use

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https://doi.org/10.1007/978-3-540-85035-9portant limitations of modularity as a community quality criterion: a resolution limit; and a bias towards finding equal-sized communities. Information-theoretic approaches that search for partitions that minimize description length are a recent alternative to modularity. This paper shows that two i

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https://doi.org/10.1007/978-3-642-14929-0blog networks; classification; community detection; computer networks; data mining; social networks

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978-3-642-14928-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE

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