graph construction
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DyNet: The Dynamic Neural Network Toolkit
• clab/cnn
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
Visualizing Large-scale and High-dimensional Data
We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space.
EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph
In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
ACL 2019
• atcbosselut/comet-commonsense
• 
We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017).
LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
#3 best model for
Skeleton Based Action Recognition
on NTU RGB+D
Towards Dynamic Computation Graphs via Sparse Latent Structure
Deep NLP models benefit from underlying structures in the data---e. g., parse trees---typically extracted using off-the-shelf parsers.
IPRE: a Dataset for Inter-Personal Relationship Extraction
Our data is the first dataset for inter-personal relationship extraction.
Low-shot learning with large-scale diffusion
CVPR 2018
• facebookresearch/low-shot-with-diffusion
• 
This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time.
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
SOTA for Graph Classification on IPC-grounded