resolutions-2019A list of data mining and machine learning papers that I implemented in 2019.
Stars: ✭ 19 (-99.56%)
Mutual labels: deepwalk, attention-mechanism, network-embedding, graph-kernel, graph-convolutional-networks, node2vec, graph-embedding, graph-classification, node-embedding
FEATHERThe reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".
Stars: ✭ 34 (-99.21%)
Mutual labels: deepwalk, network-embedding, graph-kernel, node2vec, graph-embedding, graph-classification, graph2vec, node-embedding
FSCNMFAn implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".
Stars: ✭ 16 (-99.63%)
Mutual labels: deepwalk, network-embedding, node2vec, graph-embedding, graph2vec, node-embedding
walkletsA lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).
Stars: ✭ 94 (-97.82%)
Mutual labels: deepwalk, node2vec, graph-embedding, node-embedding
EulerA distributed graph deep learning framework.
Stars: ✭ 2,701 (-37.32%)
Mutual labels: network-embedding, graph-convolutional-networks, node2vec, graph-embedding
RolXAn alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)
Stars: ✭ 52 (-98.79%)
Mutual labels: deepwalk, node2vec, graph-embedding, node-embedding
dgcnnClean & Documented TF2 implementation of "An end-to-end deep learning architecture for graph classification" (M. Zhang et al., 2018).
Stars: ✭ 21 (-99.51%)
Mutual labels: attention-mechanism, graph-embedding, graph-classification
GE-FSGGraph Embedding via Frequent Subgraphs
Stars: ✭ 39 (-99.09%)
Mutual labels: graph-embedding, graph-classification, graph-representation-learning
PDNThe official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
Stars: ✭ 44 (-98.98%)
Mutual labels: deepwalk, graph-classification, graph2vec
REGALRepresentation learning-based graph alignment based on implicit matrix factorization and structural embeddings
Stars: ✭ 78 (-98.19%)
Mutual labels: network-embedding, node-embedding
M-NMFAn implementation of "Community Preserving Network Embedding" (AAAI 2017)
Stars: ✭ 119 (-97.24%)
Mutual labels: deepwalk, node2vec
Hierarchical-attention-networkMy implementation of "Hierarchical Attention Networks for Document Classification" in Keras
Stars: ✭ 26 (-99.4%)
Mutual labels: attention-mechanism, classification-algorithm
graphkit-learnA python package for graph kernels, graph edit distances, and graph pre-image problem.
Stars: ✭ 87 (-97.98%)
Mutual labels: kernel-methods, graph-kernels
OpenANEOpenANE: the first Open source framework specialized in Attributed Network Embedding. The related paper was accepted by Neurocomputing. https://doi.org/10.1016/j.neucom.2020.05.080
Stars: ✭ 39 (-99.09%)
Mutual labels: network-embedding, graph-embedding
GNN-Recommender-SystemsAn index of recommendation algorithms that are based on Graph Neural Networks.
Stars: ✭ 505 (-88.28%)
Mutual labels: graph-convolutional-networks, graph-representation-learning
awesome-efficient-gnnCode and resources on scalable and efficient Graph Neural Networks
Stars: ✭ 498 (-88.44%)
Mutual labels: graph-convolutional-networks, graph-representation-learning
TriDNRTri-Party Deep Network Representation, IJCAI-16
Stars: ✭ 72 (-98.33%)
Mutual labels: network-embedding, graph-embedding
QGNNQuaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)
Stars: ✭ 31 (-99.28%)
Mutual labels: graph-classification, graph-representation-learning
SelfGNNA PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).
Stars: ✭ 24 (-99.44%)
Mutual labels: graph-convolutional-networks, graph-attention-networks
NMFADMMA sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Stars: ✭ 39 (-99.09%)
Mutual labels: deepwalk, node2vec
