Graph-Based Continual LearningBinh TangDavid S. MattesonInternational Conference on Learning Representations
论文理解—— Disentangle-based Continual Graph Representation Learning,程序员大本营,技术文章内容聚合第一站。
我们之前的工作 Condense and Train (CaT)是一个基于重放的 CGL 框架,具有平衡的持续学习过程,它设计了一个小而有效的内存库,用于通过压缩传入的图来重放数据。尽管CaT缓解了灾难性遗忘问题,但仍存在三个问题:(1)CaT中推导的图压缩算法仅关注标记节点,而忽略了未标记节点携带的丰富信息;(2)CaT的持续训练方案过分...
and three levels of hierarchical prototypes for storing learnt patterns. Given an input node, the AFEs will first extract basic features based on both the node attributes and its neighborhood relationship. Accordingly, the AFEs consist of two parts. One set ...
Continual graph learning (CGL) is an important and challenging task that aims to extend static GNNs to dynamic task flow scenarios. As one of the mainstream CGL methods, the experience replay (ER) method receives widespread attention due to its superior performance. However, existing ER methods ...
Despite existing continual graph learning (CGL) methods mitigating this to some extent, they predominantly operate in centralized architectures and overlook the potential of distributed graph databases to harness collective intelligence for enhanced performance optimization. To address these challenges, we ...
Disentangle-based Continual Graph Representation Learning (EMNLP, 2020) [paper][code] TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion (EMNLP, 2020) [paper][code] Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs (EMNLP, 2020) [paper][code]...
Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like...
C Yuan,Q Xie,S Ananiadou - Knowledge-Based Systems 被引量: 0发表: 2024年 Semantic-consistent learning for one-shot joint entity and relation extraction Entity and relation extraction is an essential task in knowledge acquisition and representation. Since novel relations continue to emerge in practic...
Disentangle-based Continual Graph Representation Learning (EMNLP, 2020) [paper][code] TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion (EMNLP, 2020) [paper][code] Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs (EMNLP, 2020) [paper][code]...