Distance Encoding – Design Provably More Powerful Graph Neural Networks for Structural Representation Learning
The problem of GNNs:
传统GNN会被1-WL test 所限制。因为节点都是以度进行区分的。
核心问题：节点分类或者连接预测并不是同构问题，但是GNN是基于WL-test的所以必须要给节点引入特征。
传统的WLtest会根据节点的度来区分节点，就会导致无法区分结构信息
在 ...
Heterogeneous Deep Graph Infomax
Heterogeneous Deep Graph InfomaxAbstractInspired by the emerging mutual information-based learning algorithm, This paper propose an unsupervised graph ...
Redundancy-Free Computation for Graph Neural Networks
Redundancy-Free Computation for Graph Neural NetworksMotivation
To avoid redundant computations：减少冗余计算
HAGs are functionally equivalent to standard G ...
MultiSage: Empowering GCN with Contextualized Multi-Embeddings onWeb-Scale Multipartite Networks
MultiSage: Empowering GCN with Contextualized Multi-Embeddings onWeb-Scale Multipartite NetworksAbstractExisting GCNs mostly work on homogeneous graph ...
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
AbstractProblem
Most existing HIN-based methods rely on explicit path reachability to leverage path-based semantic relatedness between users and items ...
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
Motivation现有的STOA的GCN算法不能很好的将节点特征融合进拓扑结构当中。GCN在一些节点分类的任务中不能很好的融合深层的拓扑结构和节点特征。作者希望提出一种新的GCN结构，在能保持现有的GCN优点的情况下，同样能很好的融合拓扑结构和节点的特征。
Model
把图拆成两部分，生成Topo ...
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