… pooling method for graph structured data. ASAP clusters local subgraphs hierarchically
which helps it to effectively learn the rich information present in the graph structure. We propose …

… The resulting kernel extends to graph-structured data the concept of … data are available.
Similar to CKNs, our model can also be trained end-to-end, as a GNN, leading to task-adaptive …

… Abstract : With the increase in data acquisition and storage … graph-structured data has
become a crucial issue in data … , we focus in this thesis on the study of graph-structured data. …

… deep clustering method to graph structured data processing. Deep … all information of the
graph structured data and perform self-… We hope to find an adaptive processing method, which is …

… Abstract: Keyword search is an alternative for structured languages in querying graph-structured
data… The results of each subspace are retrieved by an adaptation of the BKS algorithm. …

… dictionary adaptation (VGDA) to conduct individual structure … decompose graphs into
sub-structure such as randomwalks [… GNN can directly operate on graph-structured data to extract …

… for vector-based data such as image. To adapt it for graph-structured data, we define …
We also design adaptive L2 norm constraints on adversarial perturbations by leveraging the …

… and empirical success on graphstructured data. However, … approximation cannot learn
adaptive and structure-aware … have the ability to learn structure-aware representations. We …

… In this work, we investigate different types of distribution shifts of graph-structured data and
offer significant understanding into GDA for node classification problems. First, we show that if …

… Authors in [12] show that MInv attacks can be adapted to graph-based learning. Seeing as
… with a database D containing sensitive graph-structured data. From D a neighbouring (in this …