-equivariant function ,这里具有不同的观点。it is the author’s opinion that the formalism in this section is likely all we need to build powerful GNNs—although, of course, different perspec- tives may benefit different problems, and existence of a powerful GNN does not mean it is easy to ...
This behavior is still not fully understood, but a simple model, in which agents move randomly in space and pick up and deposit items on the basis of local information, may account for some of the characteristic features of ... E Bonabeau,M Dorigo,G Theraulaz 被引量: 0发表: 1999年 Mu...
Fiedler’s work is only valid for bidirectional communication that corresponds to undirected communication networks. In this case, it can be proved that a connected multiagent system can achieve average consensus. This is not given in the case of directed graphs (Murray, 2007). Therefore average ...
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting Forecasting of multivariate time-series is an important problem that has applications in many domains, including traffic management, cellular network configuration, and quantitative finance. In recent years, researchers...
V and E are usually taken to be finite, and many of the well-known results are not true (or are rather different) for infinite graphs because many of the arguments fail in the infinite case. The order of a graph is |V|, its number of vertices. The size of a graph is |E|, its...
Note that for typical (not fully linear) molecules and crystal unit cells with n atoms, only approximately \(\log n\) message passing steps are required to pass information to all other atoms. The information processing is facilitated by the learnable functions Ut(⋅) for node update and ...
Moreover, most existing methods consider only the connections between atoms established by chemical bonds, and thus do not fully explore the underlying relations of atoms in a molecular graph, which also highlights the key to incorporating external domain knowledge. Another neglected issue is that ...
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms
The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods o
As can be seen, our model performance is always better than GNN-PPI, even with RMSD up to 8. The comparison with 3D CNN model21 further proves the denoising ability of the hierarchical graph for protein structure errors (Supplementary Fig. 4a). In short, our model performance is not ...