Design of multi-view graph embedding using multiple kernel learning
The graph embedding is the process of representing the graph in a vector space using properties of the graphs. The existing graph embeddings rely mostly on a single property of graphs for data representation which is found to be inappropriate to capture all the characteristics of the data. Hence we designed graph embedding using multi-view approach, where each view is an embedding of the graph using a graph property. The input space of multi-view learning is then taken as the direct sum of the subspaces in which the graph embedding lie.

