a classical VAR model has to fit a parameter for each possible pair of theNbrain areas in the network, which parameters then grow with an order\(N^2\), the DCRNN learns localized filters on the structural network, also making its number of parameters independent of the network size18. This...
This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected...
Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the gr...
2023 ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach IEEE Bigdata 2023 Link Link 2023 SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily CIKM 2023 Link Link 2023 THGNN: An Embedding-based Model for Anomaly Detection...
there is no general theory for network layout, leading to a multitude of graph drawing techniques. Among these, force-directed28methods are probably the most popular visualization tools, which rely on physical metaphors. Graph layout aims to produce aesthetically appealing outputs, with many subjective...
In this work we propose MotifNet, a graph CNN capable of dealing with directed graphs by exploiting local graph motifs. We present experimental evidence showing the advantage of our approach on real data. 展开 关键词: Geometric Deep Learning Graph Convolutional Neural Networks Directed Graphs Graph...
The proposed GraphMFT models each conversation as three heterogeneous graphs, i.e., V-A graph, V-T graph, and A-T graph. Each graph includes data from only two modalities to diminish the difficulty of multimodal fusion. In addition, to handle the over-smoothing problem of graph neural ...
2.2 Variants of Graph Neural Networks 这一部分比较重要,主要介绍了经过改进后的各种变体GNN。 2.2.1 Graph Types 在这里插入图片描述 Directed Graphs 有向图。ADGPM模型为此定义了2种权重矩阵。 Heterogeneous Graphs 异构图,有多种不同类型的节点。主要介绍了GraphInception模型。
A graph neural network tailored to directed acyclic graphs that outperforms conventional GNNs by leveraging the partial order as strong inductive bias besides other suitable architectural features. - vthost/DAGNN
Data Augmentation beyond Simple Graphs Data Augmentation for Graph Imbalanced Training Learnable and Generalizable Graph Augmentation 参考 ^Songtao Liu, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, and Dinghao Wu. Local augmentation for graph neural networks. arXiv prep...