• Publications
  • Influence
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
TLDR
Two scalable representation learning models, namely metapath2vec and metapATH2vec++, are developed that are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, but also discern the structural and semantic correlations between diverse network objects. Expand
Open Graph Benchmark: Datasets for Machine Learning on Graphs
TLDR
The OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs, indicating fruitful opportunities for future research. Expand
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
TLDR
The NetMF method offers significant improvements over DeepWalk and LINE for conventional network mining tasks and provides the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Expand
Heterogeneous Graph Transformer
TLDR
The proposed HGT model consistently outperforms all the state-of-the-art GNN baselines by 9–21 on various downstream tasks, and the heterogeneous mini-batch graph sampling algorithm—HGSampling—for efficient and scalable training. Expand
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
TLDR
Graph Contrastive Coding (GCC) is designed --- a self-supervised graph neural network pre-training framework --- to capture the universal network topological properties across multiple networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations. Expand
GPT-GNN: Generative Pre-Training of Graph Neural Networks
TLDR
The GPT-GNN framework to initialize GNNs by generative pre-training introduces a self-supervised attributed graph generation task to pre-train a GNN so that it can capture the structural and semantic properties of the graph. Expand
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
TLDR
It is shown that expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale. Expand
DeepInf: Social Influence Prediction with Deep Learning
TLDR
Inspired by the recent success of deep neural networks in a wide range of computing applications, an end-to-end framework to learn users' latent feature representation for predicting social influence is designed, suggesting the effectiveness of representation learning for social applications. Expand
Inferring user demographics and social strategies in mobile social networks
TLDR
The WhoAmI method is proposed, a Double Dependent-Variable Factor Graph Model, to address the problem of double dependent-variable prediction-inferring user gender and age simultaneously, and shows that the proposed method significantly improves the prediction accuracy by up to 10% compared with several alternative methods. Expand
ProNE: Fast and Scalable Network Representation Learning
TLDR
ProNE is a fast, scalable, and effective model, whose single-thread version is 10–400× faster than efficient network embedding benchmarks with 20 threads, including LINE, DeepWalk, node2vec, GraRep, and HOPE. Expand
...
1
2
3
4
5
...