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 Networks
Abstract
Existing GCNs mostly work on homogeneous g ...
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 ...
MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
简介：对一个拥有多种节点重要性的图谱来说，如何去正确的估计他们每个节点重要性是一个很巨大的挑战。其中一个重要的问题就是如何从多种的不同的输入中有效的提取信息。针对这个问题，作者提出了一种隐向量模型，他可以从不同的信号中捕获节点的重要性。
以上图为例，针对一个电影的知识图谱来说，存在不同的种类的的节 ...