Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning(具有生成-对比学习的Motif感知黎曼图神经网络) 原文地址: https://arxiv.org/abs/2401.01232arxiv.org/abs/2401.01232 代码地址: https://github.com/RiemannGraph/MotifRGCgithub.com/RiemannGraph/MotifRGC 摘要: 图是典型...
https://github.com/RiemannGraph/MotifRGC.github.com/RiemannGraph/MotifRGC Riemannian Geometry Manifold 黎曼流形 M 是与黎曼度量耦合的光滑流形。对于每个点 x ,黎曼度量 gx 是在其切线空间 TxM 上定义的。对数映射 Logx:M→TxM 将流形中的向量转换为切线空间,而指数图 Expx 做逆变换。欧几里得空间是...
On this basis, this paper proposes a link prediction model based on motif graph neural network. The model adopts auto-encoder architecture. In the encoding process, the adjacent matrix of the node is constructed by the motif, and then the motif neighborhood of...
With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data. At present, graph convolution
A research of graph node classification23 introduces a self-attention model at the motif level that learns the weights of various motifs via differentiation. Luo et al.24 presented a graph neural network, termed as MotifGNN, which is motif-based and is utilized for recommending matches in ...
graphtensorflowmotifmetagraphgraph-convolutional-networksgraph-neural-networks UpdatedApr 29, 2021 C++ Motiflets timeseriestime-seriespatternmotifpattern-recognitionunsupervised-learningpattern-discoverymotif-discoverytime-series-analysismotifsdataseries UpdatedFeb 28, 2025 ...
In this work, we hypothesized whether human plasma DNA ends might have a preponderance of certain nucleotide contexts, i.e., preferred fragment end motifs。 即人类血浆中cfDNA的end motifs是否代表了一种独特类型的血浆DNA片段特征,血浆cfDNA的end motifs是否可以作为癌症诊断的一种指标。
network. For a subgraph with only two nodes A and B, there are three ways of how directed edges could exist between them: a connection in one direction, A→B; a connection in thereverse direction, A←B; and a bidirectional connection, A↔B. For sub-graph counting, the case that no...
graph theory, deep learning, or other approaches, to obtain a set of enriched motifs, and then determine the writers of some modification sites based on their motifs. However, such a process is unable to estimate the levels of noise within the dataset. Moreover, current motif finders are slo...
Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive learning, which cannot capture global graph structure. The key challenge to conduct subgraph-level contrastive learning is...