2022-01-19 dfd456161c6a73a2b627dc04717f15ad 99+ fast 0.1 k

TextGCN Graph Convolutional Networks for Text Classification

1.build a single text graph for a corpus based on word co-occurrence and document word relations,

2.then learn a Text Graph Convolutional Network (Text GCN) for the corpus. Our Text GCN is initialized with one-hot representation for word and document, it then jointly learns the embeddings for both words and documents, as supervised by the known class labels for documents.

TextGCN

2022-01-17 8043f9ed86fffc1eb56f1e46ce407a9d 99+ a minute 0.1 k

BertGCN Transductive Text Classification by Combining GCN and BERT

origin paper： https://arxiv.org/abs/2105.05727

ori code git： https://github.com/ZeroRin/BertGCN

官方知乎： https://zhuanlan.zhihu.com/p/378798855

TextGCN： https://arxiv.org/abs/1809.05679

图结构

we construct a heterogeneous graph containing both word nodes and document nodes following TextGCN. 如下图

node ：word nodes and document nodes

edge ： We build edges among nodes based on word occurrence in documents (document-word edges) and word co-occurrence in the whole corpus (word-word edges)

edge weight

也和TextGCN一样

node data

不同

重点是解决了TextGCN和BERT一起联调的收敛问题

GNN GCN

BertGCN

2021-12-27 559a7d56145562ddf3d527dfdf22ee3d 99+ a minute 0.1 k

ConvGNNs

ConvGNNs fall into two categories, spectral-based and spatial-based. Spectral based approaches define graph convolutions by introducing filters from the perspective of graph signal processing [82] where the graph convolutional operation is interpreted as removing noises from graph signals. Spatial-based approaches inherit ideas from RecGNNs to define graph convolutions by information propagation. spatial-based methods have developed rapidly recently due to its attractive efficiency, flexibility, and generality.

谱域图卷积是空域图卷积的特例