Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges.
We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.
Transformers have recently been applied in the more generic domain of graphs.
no code implementations • 10 Jun 2020 • Toyotaro Suzumura, Dario Garcia-Gasulla, Sergio Alvarez Napagao, Irene Li, Hiroshi Maruyama, Hiroki Kanezashi, Raquel P'erez-Arnal, Kunihiko Miyoshi, Euma Ishii, Keita Suzuki, Sayaka Shiba, Mariko Kurokawa, Yuta Kanzawa, Naomi Nakagawa, Masatoshi Hanai, Yixin Li, Tianxiao Li
At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections.
Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Ranked #4 on Dynamic Link Prediction on DBLP Temporal
We propose an incremental graph pattern matching algorithm to deal with time-evolving graph data and also propose an adaptive optimization system based on reinforcement learning to recompute vertices in the incremental process more efficiently.
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.