Here, we present a new framework (CGMega) for dissecting cancer gene module with explainable graph attention. First, we constructed a multi-omics representation graph in which nodes were genes and edges were defined as protein–protein interactions (PPIs) between genes. Node features are the conca...
Towards self-explainable graph neural network. In International Conference on Information and Knowledge Management (CIKM), 2021. 概 SE-GNN 试图构造一个本身就有可解释能力的 GNN. 符号说明 \(\mathcal{G = (V, E}, X)\),图; \(\mathcal{V} = \{v_1, \ldots, v_N\}\), nodes; \(\...
为了解决这个问题,有的研究开始学习路径的表示,例如:Leveraging meta-path based context for top- N recommendation with A neural co-attention model在实体的嵌入上使用CNN以获取path的表示向量,而Recurrent knowledge graph embedding for effective recommendation使用循环神经网络。这类方法融合基于嵌入和基于路径的方法...
解释:对Linear Model进行解释,进而解释Neural Network的局部 Reference [1] 李宏毅21版视频地址:https://www.bilibili.com/video/BV1JA411c7VT [2] 李宏毅ML官方地址:http://speech.ee.ntu.edu.tw/~tlkagk/courses.html [3] 可信图学习综述:A Survey of Trustworthy Graph Learning: Reliability, Explainability...
A PyTorch implementation of "Towards Self-Explainable Graph Neural Network" (CIKM 2021).[paper] Abstract Graph Neural Networks (GNNs), which generalize the deep neural networks to graph-structured data, have achieved great success in modeling graphs. However, as an extension of deep learning for ...
一句话总结:In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. 提出eXplainable Neural-symbolic learning (X-NeSyL) methodology:学习符号和深度表示,以及一个可解释性指标,以评估机器和人类专家解释的对齐水平。最终目标是将深...
“The data: converting tabular data to raw network graph”, “The model: graph enrichment and extraction of the graph classifier model”, “The prediction process: using visual graph classifier model (GCM)” sections. Experiments and results of the comparison with conventional machine learning ...
In this paper, we propose SCARLET (truSt and Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility features from a node's neighborhood using historical behavioral data and network structure and explain ...
In this work, we introduce a graph based deep semi-supervised framework for classifying COVID-19 from chest X-rays. Our framework introduces an optimisation model for graph diffusion that reinforces the natural relation among the tiny labelled set and the vast unlabelled data. We then connect ...
StableGNN is the framework of Graph Neural Network solutions that provide increase of stability to noise data and increase the accuracy for out-of-distribution data. It consists of three parts: graph - load and adjust data model - based of geom-gcn, with ability to include self-superised lo...