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Found 16 papers, 13 papers with code
Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
Is there a general framework that can incorporate all existing negative sampling methods?
PST-Bench: Tracing and Benchmarking the Source of Publications
Tracing the source of research papers is a fundamental yet challenging task for researchers.
OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field.
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances.
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.
GACT: Activation Compressed Training for Generic Network Architectures
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.
Rethinking the Setting of Semi-supervised Learning on Graphs
graph benchmark (IGB) consisting of 4 datasets.
GraphMAE: Self-Supervised Masked Graph Autoencoders
Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been the dominant approach in graph SSL, while the progress of generative SSL on graphs, especially graph autoencoders (GAEs), has thus far not reached the potential as promised in other fields.
Ranked #1 on
Node Classification
on Cora: fixed 20 node per class
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #3 on
Node Property Prediction
on ogbn-papers100M
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
Local Clustering Graph Neural Networks
Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.
Controllable Multi-Interest Framework for Recommendation
Recent works usually give an overall embedding from a user's behavior sequence.
Towards Knowledge-Based Recommender Dialog System
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System.
Ranked #5 on
Text Generation
on ReDial
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many real-world applications.
Ranked #1 on
Link Prediction
on Alibaba
