为了解决这些问题,本文研究了一种新颖的无监督 GSL 学习范式,即无监督图结构学习(unsupervised graph structure learning)。如图 1 所示,在本文的学习范式中,结构是由数据本身学习的,不需要任何外部指导(即标签),所获得的通用、无边缘偏差的拓扑结构可自由应用于各种下游任务。在这种情况下,自然会提出一个问题:如何为...
structure bootstrapping contrastive learning module 3.1 Graph Learner Graph Learner 生成一个带参数的图邻接矩阵。本文的 Graph Learner 包括如下四种: FGP learner Attentive Learner MLP Learner GNN Learner 3.1.1 FGP learner 通过一个参数矩阵直接建模邻接矩阵的每个元素,没有任何额外的输入。FGP Learner: ...
无监督图结构学习的必要性在于，现有的图神经网络（GNN）和深度图结构学习（Deep GSL）在有监督场景下学习图结构时存在依赖标签信息、边缘分布偏差以及限制下游任务泛化能力的问题。本文提出了一种面向无监督深度图结构学习的解决方案，旨在提供一种更实用的图结构学习范式，即无监督图结构学习。无监督图结构...
基于上述三个问题,本文提出 Simple Unsupervised Graph Representation Learning (SUGRL),框架如 Figure 2 所示:方法步骤概述:首先使用一个多层感知器(MLP)在带语义信息(semantic information)的输入 XX 上生成 Anchor embedding ; 接着基于 Graph structure AA 和Semantic feature XX 使用GCN 生成 Positive embedding...
The resulting graph structure is a symmetrical un-directed graph. An unsupervised learning approach is applied to cluster a given text corpus into groups of similar structured graphs. Moreover, if labels are given to some of the documents in the text corpus, a supervised learning approach can ...
Specifically, SCI adapts a graph-embedding method termed LINE8 to project an otherwise high-dimensional graph structure into a lower-dimensional space that describes the chromatin interactions. LINE utilizes first-order and second-order proximities of graph vertices to reduce dimensionality and promote ef...
In this chapter, we will describe a method for extracting an underlying graph structure from an unstructured text document. The resulting graph structure is a symmetrical un-directed graph. An unsupervised learning approach is applied to cluster a given text corpus into groups of similar structured ...
chemical property prediction of a molecular graph image segmentation 卷積方法產生限制的原因: Low computational efficiency due to eigendecomposition or singular value decomposition. Only showing a shallow relationship between nodes. 近年來,一個被稱為幾何深度學習(geometric deep learning) 的新興領域,將深度神...
In addition, a similarity graph is learned simultaneously to preserve the local geometrical data structure which has been confirmed critical for unsupervised feature selection. In summary, we propose a model (referred to as DGL-UFS briefly) to integrate dictionary learning, similarity graph learning ...
Beyond Redundancy Information-aware Unsupervised Multiplex Graph Structure Learning mumukehao 想读博的研究生Neurips 24 推荐指数: #paper/⭐ 领域:图融合,图增强 读者建议直接看最后一部分 模型框架 图结构限制 GNN传播为: Xv=(D~v−12A~vD~v−12)rX,Hv=σ(Xv⊙W1v)⊙W2v 其中, W1v 的所有行...