Search Results for author:
Found 4 papers, 0 papers with code
Predicting citation counts based on deep neural network learning techniques
In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index.
Towards quantitative methods to assess network generative models
Assessing generative models is not an easy task.
Learning an Integrated Distance Metric for Comparing Structure of Complex Networks
According to natural witnesses of network similarities (such as network categories) the distance metric is learned by the means of a dataset of some labeled real networks.
Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier
Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness.
