2 research outputs found
Qualifying threshold of take off stage for successfully disseminated creative ideas
The creative process is essentially Darwinian and only a small proportion of
creative ideas are selected for further development. However, the threshold
that identifies this small fraction of successfully disseminated creative ideas
at their early stage has not been thoroughly analyzed through the lens of
Rogers innovation diffusion theory. Here, we take highly cited (top 1%)
research papers as an example of the most successfully disseminated creative
ideas and explore the time it takes and citations it receives at their take off
stage, which play a crucial role in the dissemination of creativity. Results
show the majority of highly cited papers will reach 10% and 25% of their total
citations within two years and four years, respectively. Interestingly, our
results also present a minimal number of articles that attract their first
citation before publication. As for the discipline, number of references, and
Price index, we find a significant difference exists: Clinical, Pre-Clinical &
Health and Life Sciences are the first two disciplines to reach the C10% and
C25% in a shorter amount of time. Highly cited papers with limited references
usually take more time to reach 10% and 25% of their total citations. In
addition, highly cited papers will attract citations rapidly when they cite
more recent references. These results provide insights into the timespan and
citations for a research paper to become highly cited at the take off stage in
its diffusion process, as well as the factors that may influence it.Comment: 17 page
SSNE: Effective Node Representation for Link Prediction in Sparse Networks
Graph embedding is gaining its popularity for link prediction in complex
networks and achieving excellent performance. However, limited work has been
done in sparse networks that represent most of real networks. In this paper, we
propose a model, Sparse Structural Network Embedding (SSNE), to obtain node
representation for link predication in sparse networks. The SSNE first
transforms the adjacency matrix into the Sum of Normalized -order Adjacency
Matrix (SNHAM), and then maps the SNHAM matrix into a -dimensional feature
matrix for node representation via a neural network model. The mapping
operation is proved to be an equivalent variation of singular value
decomposition. Finally, we calculate nodal similarities for link prediction
based on such feature matrix. By extensive testing experiments bases on
synthetic and real sparse network, we show that the proposed method presents
better link prediction performance in comparison of those of structural
similarity indexes, matrix optimization and other graph embedding models.Comment: 11 pages, 4 figures, 3 Table