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Greatest papers with code

Diffusion Improves Graph Learning

NeurIPS 2019 rusty1s/pytorch_geometric

In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).

GRAPH LEARNING NODE CLASSIFICATION

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

3 Sep 2019dmlc/dgl

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.

GRAPH LEARNING NODE CLASSIFICATION

Graph Agreement Models for Semi-Supervised Learning

NeurIPS 2019 tensorflow/neural-structured-learning

To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features.

GRAPH LEARNING NODE CLASSIFICATION

Graph-RISE: Graph-Regularized Image Semantic Embedding

14 Feb 2019tensorflow/neural-structured-learning

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.

GRAPH LEARNING IMAGE CLASSIFICATION IMAGE RETRIEVAL

Collaborative Similarity Embedding for Recommender Systems

17 Feb 2019cnclabs/smore

We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation.

GRAPH LEARNING RECOMMENDATION SYSTEMS REPRESENTATION LEARNING

Relational Graph Learning for Crowd Navigation

28 Sep 2019vita-epfl/CrowdNav

We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future.

GRAPH LEARNING

PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

16 Apr 2020wenwenyu/PICK-pytorch

Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently.

GRAPH LEARNING OPTICAL CHARACTER RECOGNITION

Accurate, Efficient and Scalable Graph Embedding

28 Oct 2018GraphSAINT/GraphSAINT

However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -- state-of the art techniques are unable to achieve scalability without losing accuracy and efficiency.

GRAPH EMBEDDING GRAPH SAMPLING NODE CLASSIFICATION

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

10 Apr 2019benedekrozemberczki/SEAL-CI

We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.

GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH LEARNING NODE CLASSIFICATION