117 citations · 427 across the 32 of their papers we have counts for
8 papers · 1 filter
A Review for Weighted MinHash Algorithms
Wei Wu, Bin Li, Ling Chen +2
Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data min…
SINE: Scalable Incomplete Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content.…
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together
Tao Shen, Tianyi Zhou, Guodong Long +2
Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, th…
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
Tao Shen, Tianyi Zhou, Guodong Long +2
Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture l…
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result…
Adversarially Regularized Graph Autoencoder for Graph Embedding
Shirui Pan, Ruiqi Hu, Guodong Long +3
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the…