A.1 Prototype-based Graph Information Bottleneck - Eq. 4 From Eq. 3, the GIB objective is: min We perform ablation studies to examine the effectiveness of our model (i.e., PGIB and PGIB In Figure 7, the " with all " setting represents our final model that includes all the components. We conduct experiments on graph classification using different readout functions for PGIB. We illustrate the reasoning process on two datasets, i.e., MUT AG and BA2Motif, in Figure 8. PGIB Then, PGIB computes the "points contributed" to predicting each class by multiplying the similarity We have conducted additional qualitative analysis. It is crucial that the prototypes not only contain key structural information from the input graph but also ensure a certain level of diversity since each class is represented by multiple prototypes. Its goal is to make the masked subgraph's prediction as close as possible to the original graph, which helps to detect substructures significant

A long standing goal in neuroscience has been to elucidate the functional organization of the brain.

Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine.

The diagram of our proposed Neural Lad framework is illustrated in Fig.1. The pseudo code of the proposed Neural Lad is described in Alg. 1. The training time of Neural Lad for the toy dataset is about 8s per epoch. It is worth noting that we use larger weight decay for PhysioNe sepsis dataset to avoid over-fitting. Visualization of memory network enhanced scores.