How to generate semantic embeddings? The semantic embeddings, i.e. $\mathcal {Z}$ in the paper, are generated by metapath2vec algorithm. Users may refer to …

Join us for our annual HGSL General Board Meeting this Wednesday, February 08, 2023 from 7:00-7:30pm. Check out Softball Podcasts! Parents and Coaches! Find lots of valuable …

Heterogeneous Graph Structure Learning for graph neural networks, and propose a novel framework HGSL. In HGSL, the heterogeneous graph and the GNN parameters are jointly learned towards better node classification performance.

How to generate semantic embeddings? The semantic embeddings, i.e. Z Z in the paper, are generated by metapath2vec algorithm. Users may refer to https://github.com/dmlc/dgl/tree/master/examples/pytorch/metapath2vec for an implementation.

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本文首次对图神经网络的异质图结构学习进行研究，并提出了一个异质图结构学习框架HGSL。 HGSL框架根据下游任务对异质图结构和GNN参数进行联合学习。 在图结构学习部分，HGSL分别对每个关系子图进行学习。 具体来说，对于每一种关系，通过从异质节点特征和图结构中挖掘复杂关联，生成特征图、和语义图并与原始图结构进一步融合成可学习的异质图结构馈送给GNN。 最终，图结构学习参数和GNN参数联合优化以完成节点分类任务。 一个异质图. 的邻接矩阵由多 …

2021年5月25日 · 目标：异质网络（子图）的嵌入,用于节点分类任务方法：根据边（R）类型的的不同分解成多个子网络，对每个子网络使用GCN，进行联合优化，目标为是最小化交叉熵（目标是节点分类）具体流程如图：一、得到关系r1的子图对于一类关系 r1 ，指的是连接两个节点的边，将这类关系的节点从异质图上挖下来构成一个子图。 该子图的邻接矩阵为Ar1。 另外还融入了节点属性信息和节点间的语义信息。 二、得到融合节点属性的子图对于一个关系 r1 ，节点的属性信 …

2022年3月5日 · HGSL 通过挖掘特征相似性、特征与结构之间的交互以及异质图中的高阶语义结构来生成适合下游任务的异质图结构并联合学习 GNN参数。 三个数据集上的实验结果表明，HGSL 的性能优于基线 模型。 许多真实世界的数据具有图结构，例如社交媒体图、文献引用图。 图神经网络（GNN）作为一种处理图数据的强大深度表示学习工具被广泛地应用于节点分类、图分类以及推荐等下游任务中。 最近，随着真实世界中异质图应用的激增，学者们提出了异质图神经网 …

2021年5月18日 · Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. The success of the existing HGNNs relies on one fundamental assumption, i.e., the original heterogeneous graph structure is reliable.

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