5 research outputs found
Personalized community detection in scholarly network
Most graph clustering methods partition a network into communities based solely on the topology and structure of the network. Due to this, the means via which communities are detected on a network are insensitive to the preferences of a user who is searching the network with a specific, personalized information need. Such partition algorithms may be of diminished value for scholars exploring networks of research if these scholars possess prior preferences on what information they consider relevant. To better address this type of information seeking behavior, we introduce a personalized community detection algorithm that provides higher-resolution partitioning of areas of the network that are more relevant to a provided seed query. This algorithm utilizes the divisive Girvan-Newman approach but incorporates a user's personal preferences as a prior. We show that this personalized algorithm can produce a more fine-tuned partition of a scholarly network when compared to existing prior-insensitive approaches
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Novelty and influence of creative works, and quantifying patterns of advances based on probabilistic references networks
Funder: BK21 Plus Postgraduate Organization for Content ScienceAbstractRecent advances in the quantitative, computational methodology for the modeling and analysis of heterogeneous large-scale data are leading to new opportunities for understanding human behaviors and faculties, including creativity that drives creative enterprises such as science. While innovation is crucial for novel and influential achievements, quantifying these qualities in creative works remains a challenge. Here we present an information-theoretic framework for computing the novelty and influence of creative works based on their generation probabilities reflecting the degree of uniqueness of their elements in comparison with other works. Applying the formalism to a high-quality, large-scale data set of classical piano compositions–works of significant scientific and intellectual value–spanning several centuries of musical history, represented as symbolic progressions of chords, we find that the enterprise’s developmental history can be characterised as a dynamic process composed of the emergence of dominant, paradigmatic creative styles that define distinct historical periods. These findings can offer a new understanding of the evolution of creative enterprises based on principled measures of novelty and influence.</jats:p