Graph neural networksSociological theoriesWeighted signed networksLink predictionNetwork embedding aims to map nodes in a network to low-dimensional vector representations. Graph neural networks (GNNs) have received much attention and have achieved state-of-the-art performance in learning node representation...
This study explores the bipartite secure synchronization problem of coupled quaternion-valued neural networks (QVNNs), in which variable sampled communications and random deception attacks are considered. Firstly, by employing the signed graph theory, the mathematical model of coupled QVNNs with structur...
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations Kivilcim BB, Ertugrul IO et al (2018) Modeling brain networks with artificial neural networks. In: Graphs in biomedical image analysis and integrating medi...
machine-learningpytorchsigned-networksgraph-neural-networksmagnetic-laplaciandirected-networkssigned-directed-networks UpdatedFeb 1, 2023 Python Python Implementation for Signed Random Walk with Restart (SRWR) pythonrandom-walk-with-restartsigned-networkssigned-random-walk-with-restartpersonalized-ranking ...
Recently, graph neural networks (GNNs) have handled these interactions successfully and shown great predictive performance, but most computational approaches are built on an unsigned graph that commonly represents assortative relations between similar nodes. Semantic correlation between drugs, such as ...
Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and therefore growing interest, is the usage of graph convolutional neural networks (GCNs). ...
Signed network embedding methods allow for a low-dimensional representation of nodes and primarily focus on partitioning the graph into clusters, hence losing information on continuous node attributes. Here, we introduce a spectral embedding algorithm fo
Our work consists of three steps: basic protein sequence coding, graph-based feature extraction model, and the final neural network classifier. Firstly, we transform raw protein sequences into fixed-length coding using the conjoint triad (CT) method. Next, we propose an improved weighted variationa...
Brox. Octree gen- erating networks: Efficient convolutional architectures for high-resolution 3d outputs. In ICCV, 2017. [50] N. Verma, E. Boyer, and J. Verbeek. Feastnet: Feature- steered graph convolutions for 3d shape analysis. [51] Y. Wang, Y. ...
First, we calculate the positive and negative probability of the walker teleporting fromuto the nodevi(i = 1, …,m) in the graphGk. The way to calculate the positive and negative probability is the same except that they are based on different kinds of links. ...