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Abstract: Recently, graph convolutional networks(GCNs) have drawn increasing attention in many domains, e.g., social networks, recommendation systems.

Oct 28, 2021 · To take into account such uncertainty in graph topology, we propose in this paper a dynamic graph convolution network (DyGCN), where edge weights are treated as learnable parameters. A novel adaptive edge dropping (AdaDrop) strategy is developed for DyGCN, such that even graph topology can be optimized.

Jan 01, 2020 · First neural network approaches to classify dynamic graph-structured data. • We propose two novel techniques: WD-GCN and CD-GCN. • These techniques are based on combination of graph convolutional units and LSTM. • Semi-supervised classification of sequence of vertices. • Supervised classification of sequence of graphs. Abstract

Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change over time. The goal is to exploit existing neural network architectures to model datasets that are best represented with graph structures that change over time.

Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation. Yan Zhang, Zhijiang Guo, Zhiyang Teng, Wei Lu, Shay B. Cohen, Zuozhu Liu, ...

The dynamic graph convolution can dynamically capture the potential relationship between human actions and assembly tools in assembly behavior ...

These exam- ples urge the development of dynamic graph methods that encode the temporal evolution of relational data. Built on the recent success of graph ...

28.10.2021 · To take into account such uncertainty in graph topology, we propose in this paper a dynamic graph convolution network (DyGCN), where edge weights are treated as learnable parameters. A novel adaptive edge dropping (AdaDrop) strategy is developed for DyGCN, such that even graph topology can be optimized.

The Concatenated Dynamic-GC layer, which performs at each step of the sequence a graph convolution on the vertex input features and concatenates it to the input ...

An invertible graph convolutional network is designed for disease classification based on brain connectivity networks. It is capable of ...

Dynamic graph convolutional networks with attention mechanism for rumor detection on social media Abstract Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely.

18.08.2021 · In this study, motivated by the dynamic nature of rumor propagation, we present a novel graph convolutional network-based model, named Dynamic GCN, to better understand the evolving pattern of rumor propagation. The model includes two distinct ways of representing rumor propagation with graph snapshots: sequential and temporal snapshots.

Apr 20, 2017 · Dynamic Graph Convolutional Networks 04/20/2017 ∙ by Franco Manessi, et al. ∙ 0 ∙ share Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time.

new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calcu- lated graph features. We also develop a …

04.12.2021 · A novel dynamic multi-graph convolution recurrent network (DMGCRN) is proposed, which can model the spatial correlations of distance, the spatial correlation of structure, and the temporal correlations simultaneously. Traffic forecasting is a problem of intelligent transportation systems (ITS) and crucial for individuals and public agencies.

24.01.2018 · To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures.

Apr 20, 2017 · Dynamic Graph Convolutional Networks Franco Manessi, Alessandro Rozza, Mario Manzo Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time.

01.01.2020 · First neural network approaches to classify dynamic graph-structured data. • We propose two novel techniques: WD-GCN and CD-GCN. • These techniques are based on combination of graph convolutional units and LSTM. • Semi-supervised classification of sequence of vertices. • Supervised classification of sequence of graphs. Abstract

01.02.2022 · To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation.

05.05.2022 · The dynamic graph convolution can dynamically capture the potential relationship between human actions and assembly tools in assembly behavior images, further enhancing the model’s characterization ability. This paper is organized as follows. Section "Related works" summarizes the related research work for assembly behavior recognition.

Abstract: Many different classification tasks need to manage structured data, which are usually modeled as graphs.

The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was ...

May 05, 2022 · Ye et al. 30 proposed to use dynamic graph convolution to dynamically generate a specific graph structure for each image, which improved the generalization ability of the network model to a certain extent. Attention mechanism can help the graph convolutional networks to focus on the key information.

20.04.2017 · [Submitted on 20 Apr 2017] Dynamic Graph Convolutional Networks Franco Manessi, Alessandro Rozza, Mario Manzo Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time.