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论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

开发技术 开发技术 2022-10-19 次浏览

论文信息

论文标题:Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
论文作者:Yizhen Zheng, Shirui Pan, Vincent Cs Lee, Yu Zheng, Philip S. Yu
论文来源:2022,NeurIPS
论文地址:download 
论文代码:download 

1 Introduction

  GCL 需要大量的 Epoch 在数据集上训练,本文的启发来自 GCL 的代表性工作 DGI 和 MVGRL,因为 Sigmoid 函数存在的缺陷,因此,本文提出  Group Discrimination (GD) ,并基于此提出本文的模型 Graph Group Discrimination (GGD)。

  Graph ContrastiveLearning 和 Group Discrimination 的区别:

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  • GD directly discriminates a group of positive nodes from a group of negative nodes.
  • GCL maximise the mutual information (MI) between an anchor node and its positive counterparts, sharing similar semantic information while doing the opposite for negative counterparts.

  贡献:

  • 1) We re-examine existing GCL approaches (e.g., DGI and MVGRL), and we introduce a novel and efficient self-supervised GRL paradigm, namely, Group Discrimination (GD).
  • 2) Based on GD, we propose a new self-supervised GRL model, GGD, which is fast in training and convergence, and possess high scalability.
  • 3) We conduct extensive experiments on eight datasets, including an extremely large dataset, ogbn-papers100M with billion edges.

2 Rethinking Representative GCL Methods

  本节以经典的 DGI 、MVGRL 为例子,说明了互信息最大化并不是对比学习的贡献因素,而是一个新的范式,群体歧视(group discrimination)。

2.1 Rethinking GCL Methods

  回顾一下 DGI :

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  代码:

论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》
class DGI(nn.Module):
    def __init__(self, g, in_feats, n_hidden, n_layers, activation, dropout):
        super(DGI, self).__init__()
        self.encoder = Encoder(g, in_feats, n_hidden, n_layers, activation, dropout)
        self.discriminator = Discriminator(n_hidden)
        self.loss = nn.BCEWithLogitsLoss()

    def forward(self, features):
        positive = self.encoder(features, corrupt=False)
        negative = self.encoder(features, corrupt=True)
        summary = torch.sigmoid(positive.mean(dim=0))
        positive = self.discriminator(positive, summary)
        negative = self.discriminator(negative, summary)
        l1 = self.loss(positive, torch.ones_like(positive))
        l2 = self.loss(negative, torch.zeros_like(negative))
        return l1 + l2
View Code

  本文研究 DGI 结论:一个 Sigmoid 函数不适用于权重被 Xavier 初始化的 GNN 生成的 summary vector,且 summary vector  中的元素非常接近于相同的值。

  接着尝试将 Summary vector 中的数值变换成不同的常量 (from 0 to 1):

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  结论:

    • 将 summary vector 中的数值变成 0,求解相似度时导致所有的 score 变成 0,也就是 postive 项的损失函数变成 负无穷,无法优化;
    • summary vector 设置其他值,导致 数值不稳定;

  DGI 的简化:

  ① 将 summary vector 设置为 单位向量(缩放对损失不影响);

  ② 去掉 Discriminator (Bilinear​ :先做线性变换,再求内积相似度)的权重向量;【双线性层的 $W$ 其实就是一个线性变换层】

    $begin{aligned}mathcal{L}_{D G I} &=frac{1}{2 N}left(sumlimits _{i=1}^{N} log mathcal{D}left(mathbf{h}_{i}, mathbf{s}right)+log left(1-mathcal{D}left(tilde{mathbf{h}}_{i}, mathbf{s}right)right)right) \&left.=frac{1}{2 N}left(sumlimits_{i=1}^{N} log left(mathbf{h}_{i} cdot mathbf{s}right)+log left(1-tilde{mathbf{h}}_{i} cdot mathbf{s}right)right)right) \&=frac{1}{2 N}left(sumlimits_{i=1}^{N} log left(operatorname{sum}left(mathbf{h}_{i}right)right)+log left(1-operatorname{sum}left(tilde{mathbf{h}}_{i}right)right)right)end{aligned} quadquadquad(1)$

  Bilinear :

    $mathcal{D}left(mathbf{h}_{i}, mathbf{s}right)=sigma_{s i g}left(mathbf{h}_{i} cdot mathbf{W} cdot mathbf{s}right)quadquadquad(2)$

  实验:替换 $text{Eq.1}$ 中的 aggregation function ,即 sum 函数

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  替换形式为:

    $mathcal{L}_{B C E}=-frac{1}{2 N}left(sumlimits _{i=1}^{2 N} y_{i} log hat{y}_{i}+left(1-y_{i}right) log left(1-hat{y}_{i}right)right)quadquadquad(3)$

  其中,$hat{y}_{i}=operatorname{agg}left(mathbf{h}_{i}right)$ ,$y_{i} in mathbb{R}^{1 times 1}$ ,$hat{y}_{i} in mathbb{R}^{1 times 1}$。论文中阐述 $y_{i}$ 和 $hat{y}_{i}$ 分别代表 node $i$ 是否是 postive sample ,及其预测输出。Q :当 aggregation function 采用 $text{mean}$ 的时候,对于 postive  sample $i$ ,$hat{y}_{i}$ 值会趋于 $1$ 么?

  DGI 真正所做的是区分正确拓扑生成的一组节点和损坏拓扑生成的节点,如 Figure 1 所示。可以这么理解,DGI 是使用一个固定的向量 $s$ 去区分两组节点嵌入矩阵(postive and negative)。

  为解决上述 GD 的问题,本文将考虑使用 $text{Eq.3}$ 去替换 DGI 中的损失函数。替换的好处:节省显存和加快计算速度,对于精度没啥改变,说的天花乱坠。

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  Note:方差大的稍微大一点的 method ,就是容易被诋毁。

  Group Discrimination 定义:GRL method,将不同组别的节点划分给不同的组,对于 postive pair 和 negative pair 分别划分到 "1" 组 和 "0" 组。

3 Methodology 

  整体框架:

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  组成部分

    • Siamese Network :模仿 MVGRL 的架构;
    • Data Augmentation :提供相似意义信息,带来的是时间成本;【dropout edge、feature mask】
    • Loss function : $text{Eq.3}$;
  模型推断:

  首先:固定 GNN encoder、MLP predict 的参数,获得初步的节点表示 $mathbf{H}_{theta}$;

  其次:MVGRL 多视图对比工作给本文深刻的启发,所以考虑引入全局信息 :$ mathbf{H}_{theta}^{text {global }}=mathbf{A}^{n} mathbf{H}_{theta}$;

  最后:得到局部表示和全局表示的聚合 $mathbf{H}=mathbf{H}_{theta}^{text {global }}+mathbf{H}_{theta}$ ;

4 Experiments

4.1 Datasets

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

4.2 Result

节点分类

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

训练时间 和 内存消耗

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

4.3 Evaluating on Large-scale datasets

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

 

5 Future Work

   For example, can we extend the current binary Group Discrimination scheme (i.e., classifying nodes generated with different topology) to discrimination among multiple groups?

  论文解读(GGD)《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

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