目录
- 读论文
读论文
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
看不懂
Heterogeneous Graph Transformer
Meta Relation
每条边映射到一个三元组
Dynamic Heterogeneous Graph
把时间戳赋值到每条边上，每个点也可以assign不同的时间戳
General GNN
从 \(l-1\) 层到 \(l\) 层：
\[H^l[t] \leftarrow \underset{\forall s \in N(t), \forall e \in E(s, t)}{\text { Aggregate }}\left(\operatorname{Extract}\left(H^{l-1}[s] ; H^{l-1}[t], e\right)\right) \]Heterogeneous GNNs
好像要用 interaction matrices 来共享一部分 weights
HETEROGENEOUS GRAPH TRANSFORMER
Its idea is to use the meta relations of heterogeneous graphs to parameterize weight matrices for the heterogeneous mutual attention, message passing, and propagation steps.
general attention-based GNNs
\[H^l[t] \leftarrow \underset{\forall s \in N(t), \forall e \in E(s, t)}{\text { Aggregate }}(\text { Attention }(s, t) \cdot \text { Message }(s)) \]\[\begin{aligned} \operatorname{Attention}_{G A T}(s, t) & =\underset{\forall s \in N(t)}{\operatorname{Softmax}}\left(\vec{a}\left(W H^{l-1}[t] \| W H^{l-1}[s]\right)\right) \\ \operatorname{Message}_{G A T}(s) & =W H^{l-1}[s] \\ \operatorname{Aggregate}_{G A T}(\cdot) & =\sigma(\operatorname{Mean}(\cdot)) \end{aligned} \]HGT计算方式
注意他矩阵W都是type-aware的
\[\begin{gathered} \operatorname{Head}_k^{A T T}(i, j)=\left(\frac{\mathbf{K}_i^k \mathbf{W}_{\psi(i, j)}^{A T T} \mathbf{Q}_j^{k^{\mathrm{T}}}}{\sqrt{d}}\right) \mu(\phi(i), \psi(i, j), \phi(j)) \\ \operatorname{Attention}(i, j)=\operatorname{Softmax}_{i \in N(j)}\left(\|_k \operatorname{Head}_k^{A T T}(i, j)\right) \end{gathered} \]\[\begin{gathered} \operatorname{Message}(i, j)=\|_k \mathbf{W}_{\phi(i)}^k \mathbf{h}_i \mathbf{W}_{\psi(i, j)}^{M S G} \\ \mathbf{h}_j=\sum_{i \in N(j)} \operatorname{Attention}(i, j) \odot \operatorname{Message}(i, j) \end{gathered} \]后面还有时间编码和HGSampling
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
Network Schema: 一个paradigm
Metapath：元路径
Heterogeneous Graph Embedding
convolution-based HGNN
思想是把周围点的信息aggregate到当前点，形成embedding
更高效但是参数更多空间消耗更大
HAN
在一个metapath的相邻节点之中，attention和embedding相互计算
获得多个metapath的 different semantic node embeddings 之后，再过一遍（mlp，激活函数，attention vector）计算attention，再计算最后的embedding
HAHE：uses cosine similarity instead of attention mechanism to calculate the two kinds of importance
MAGNN:把metapath的中间节点也用encoder存进了semantic信息，不过他encoder到底怎么工作的没仔细说不太懂
这几种都要人工设置metapath
GTN
Graph Transformer Networks，分出subgraphs然后学习embedding
But GTN only considers edge types with ignoring diferent types of nodes.
HetSANN
Heterogeneous Graph Structural Attention Neural Network
首先把neighbor nodes映射进选中的点的空间，然后通过type-aware attention layer来学习每个点的embedding，这样可以考虑周围点的类型不同以及重要性不同
HGT
同论文
HetSANN和HGT都使用分层注意力机制来代替metapath，但是生成了更多的参数
HGCN
认为异质图是由多个二分图子图构成的，在每个子图上用GCN，再用attention（type-aware）aggregate起来获得最终的embedding
Autoencoder‑based approaches
HIN2Vec
尝试算出i和j有关系r的概率（conditional probability）
\[P(r|i,j) = sigmoid\left(\sum\mathbf{W}_I\vec i \cdot \mathbf{W}_J\vec j\cdot\mathbf{W}_R\vec r\right) \]然后计算交叉熵损失，虽然不太懂他L(i,j,r)到底是什么
SHINE
用三个autoencoder计算三种不同的语义信息
utilizes the topological structure of heterogeneous graphs
HNE
每个点代表text或者image，adopts CNN and fully connected layers
NSHE
首先使用GCN（还是先project成向量，然后aggregate）来涵盖一阶proximity，然后使用多个autoencoder最后拼起来进行预测任务
标签：attention,mathbf,Graph,论文,right,Heterogeneous,operatorname From： https://www.cnblogs.com/lcyfrog/p/17658703.html