Network Embedding via Motifsdoi:10.1145/3473911Motifnetwork embeddingmotif super-vertexmotif embeddingNetwork embedding has emerged as an effective way to deal with downstream tasks, such as node classification [16, 31, 42]. Most existing methods leverage multi-similarities between nodes such as ...
在这篇文章中,我们提出了一个新的算法框架:Learning Embeddings based on Motifs Of the Network (LEMON)。 LEMON将各种motifs作为超顶点放入网络中,构建了一个由motif超顶点与网络中原始顶点之间的关系组成的异构网络。此外,为了将异构网络整合到 Skip-gram 模型 [27] 中,我们提出了一种主题步随机游走策略,以确保...
This embedding method is adopted in motif convolutional networks [34] which uses an attention mechanism and an epsilon greedy strategy to select only one neighbour for each node’s neighbourhood aggregation. With a focus on heterogeneous graphs, motif-CNN [35] also uses motifs to capture higher-...
FastGCN(fast learning with graph convolutional networks via importance sampling,ICLR 2018)对每个图卷积层采样固定数量的节点,而不是像GraphSage那样对每个节点采样固定数量的邻居。它将图卷积理解为节点embedding函数在概率测度下的积分变换。采用蒙特卡罗近似和方差减少技术来简化训练过程。由于FastGCN对每个层独立地采样节...
(WT) cells,CebpaKO cells andCebpeKO cells in the force-directed graph embedding space. Estimated kernel density data are shown as a contour line on a scatter plot to depict cell density.e, Cell-type proportions in the WT and ground-truth KO samples. Gra, granulocyte; KDE, kernel density ...
structures and properties that are not considered in the existing NRL approaches. Network motifs and hypernetwork embedding, used to naturally indicate richer relationships among nodes, are among complex structures to be incorporated into an embedding space in an effective manner to provide more ...
(WT) cells,CebpaKO cells andCebpeKO cells in the force-directed graph embedding space. Estimated kernel density data are shown as a contour line on a scatter plot to depict cell density.e, Cell-type proportions in the WT and ground-truth KO samples. Gra, granulocyte; KDE, kernel density ...
As an example, a random embedding is shown where the size of a node corresponds to the degree of centrality of that node. Full size image To provide a suitable prior of a non-networked fluid where structure is dominated by packing considerations alone, we performed graph-theoretic analyses on...
The learning of node representation vectors involves aggregating multi-dimensional features via GCN to acquire each node embedding vector representation \(\{ H_{T}^{1} ,H_{T}^{2} , \ldots ,H_{T}^{N} \}\), where N signifies the number of nodes. In this study, an improved LSTM ...
While not having ramifications for the notion of s-distance, the finer classes of s-walks above provide a means, within the s-walk framework, to define high-order substructures or motifs that cannot be determined from the s-line graph. To define an example of these substructures, we require...