Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2016)
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Updated
Dec 29, 2015 - Java
Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2016)
Graph Classification on Control Flow Graph by Support Vector Machine
A Repository of Benchmark Graph Datasets for Graph Classification (31 Graph Datasets In Total).
Data Mining course projects
A convolutional neural network for graph classification in PyTorch
A list of data mining and machine learning papers that I implemented in 2019.
Clean & Documented TF2 implementation of "An end-to-end deep learning architecture for graph classification" (M. Zhang et al., 2018).
Tool to convert datasets from "Benchmark Data Sets for Graph Kernels" (K. Kersting et al., 2016) into a format suitable for deep learning research.
NAG-FS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification.
NAGFS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification code, recoded by Dogu Can ELCI.
Graph Embedding via Frequent Subgraphs
Fast embedding-based graph classification with connections to kernels
HADA (Hiearachical Adversarial Domain Alignment) for brain graph prediction and classification.
AAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
k-hop Graph Neural Networks
Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)
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