웹2024년 7월 1일 · BA-SHAPES dataset from GNNExplainer: Generating Explanations for Graph Neural Networks. This is a synthetic dataset for node classification. It is generated … 웹2024년 7월 7일 · GNNExplainer outperformed the other baselines on BA-SHAPES, BA-COMMUNITY, and Tree-GRID but not TREE-CYCLES. The proposed TraP2 surpassed all alternative methods across all four datasets, achieving up to 0.02 and 0.134 higher fidelity than GNNExplainer on the difficult tasks of BA-COMMUNITY and TREE-CYCLES, …
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웹In this work, we propose a novel method, known as MotifExplainer, to explain GNNs by identifying important motifs, recurrent and statistically significant patterns in graphs. Our proposed motif-based methods can provide better human-understandable explanations than methods based on nodes, edges, and regular subgraphs. 웹2024년 4월 10일 · The Synthetic datasets used in Parameterized Explainer for Graph Neural Network. It takes Barabási–Albert(BA) graph or balance tree as base graph and randomly … dr hernz media pa
Towards Explainable Graph Neural Networks 码农网
웹2012년 12월 24일 · The direction of the edges and the sub-parts of the model identified by each node are shown in (b). noise, allowing the construction of a not necessarily connected common subgraph, as shown in figure 2(c), that enables the recognition of similar sub-parts even if the overall objects shape/structure is dissimilar. 2 Results and Discussion The … 웹Calles. Subgerencia de Información Geoespacial. DG Ciencias de la Información. SS de Políticas Públicas Basadas en Evidencia. Secretaría de Innovación y Transformación Digital. Jefatura de Gabinete de Ministros Listado de las calles … 웹2024년 4월 13일 · This dataset is composed of 2D 9 geometric shapes, each shape is drawn randomly on a 200x200 RGB image. During the generation of this dataset, the perimeter and the position of each shape are selected randomly and independently for each image, the rotation angle of each shape is selected randomly for each image within an interval … dr. herodotou shallotte nc