从结果看,当隐因子的个数从4->10,DisenGCN的性能增益逐渐增加,这说明解耦工作的重要性。当K>12, graph复杂度过高,此时DisenGCN也变得无能为力。 下图将在8个隐因子影响的graph中学到的64维的节点emb的元素相关性(element)结果可视化,DisenGCN采用8个channel。结果显示,DisenGCN有八个明显的对角线块,这说明Disen...
Code:https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering 这篇文章主要借鉴了ICML 2019的一篇文章的工作:Disentangled Graph Convolutional Networks。 动机 这篇文章主要是将ICML 2019的工作应用在了推荐上,用户-物品交互关系的建模发展过程可以概括为① 单个ID(用户、物品)的embedding ② 融入个...
However, learning representations that disentangle the latent factors poses great challenges and remains largely unexplored in the literature of graph neural networks. In this paper, we introduce the disentangled graph convolutional network (DisenGCN) to learn disentangled node representations. In particular...
Official Code for "Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction (AAAI 2021)" ihbae.com/publication/dmrgcn/ Topics deep-learning multi-agent autonomous-vehicles human-trajectory-prediction motion-forecasting disentangled-graph-neural-networks aaai2021 Resource...
Intent-aware Recommendation via Disentangled Graph Contrastive Learning论文阅读笔记 Abstract 存在的问题: 如何学习复杂和多样的意图,特别是当用户的行为在现实中通常不充分时 是不同的行为具有不同的意图分布,因此如何建立它们之间的关系,以建立一个更可解释的推荐系统。 本文方法: 在本文中,我们提出了通过解耦...
Recently, Graph Convolutional Networks (GCNs) have shown remarkable success in the node classification task, due to the ability to aggregate neighborhood information and propagate supervised signals over the graph. However, most GCN-style models require relatively sufficient labeled data, which are not ...
Disentangled Graph Convolutional NetworksJianxin MaPeng CuiKun KuangXin WangWenwu ZhuPMLRInternational Conference on Machine Learning
Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction.Inhwan BaeHae-Gon JeonNational Conference on Artificial Intelligence
The invention discloses a method and device for constructing a graph convolutional neural network for learning disentangled representations, the method comprising: performing probability modeling of a formation process of an input graph, and generating a probability generation model describing a plurality ...
In this study, we introduced the disentangled prototypical graph convolutional network (DPGCN) for identifying fraudulent Ethereum transactions. Our approach combines the strengths of prototypical networks, disentangled representations, and graph convolutional networks for effective transaction network modeling...