Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Link prediction is a key problem for network-structured data. Link prediction heuristics use
some score functions, such as common neighbors and Katz index, to measure the likelihood …

Google's PageRank method was developed to evaluate the importance of web-pages via
their link structure. The mathematics of PageRank, however, are entirely general and apply …

Deep models can be made scale-invariant when trained with multi-scale information.
Images can be easily made multi-scale, given their grid-like structures. Extending this to …

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs.
For a pair of nodes and, the PPR value equals the probability that an-discounted random …

The location-aware keyword query returns ranked objects that are near a query location and
that have textual descriptions that match query keywords. This query occurs inherently in …

Industrial Applications of Machine Learning shows how machine learning can be applied to
address real-world problems in the fourth industrial revolution, and provides the required …

The notions of subgraph centrality and communicability, based on the exponential of the
adjacency matrix of the underlying graph, have been effectively used in the analysis of …

Senegal is among the few African countries that counts with an important agroecological
movement. This movement is strongly backed up by a network of transnational partnerships …

PageRank is a famous measure of graph centrality that has numerous applications in
practice. The problem of computing a single node's PageRank has been the subject of …