2 citations · 5 across the 5 of their papers we have counts for
5 papers
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs
Ruibin Zeng, Minglong Lei, Lingfeng Niu +1
Combinatorial optimization (CO) on graphs is a classic topic that has been extensively studied across many scientific and industrial fields. Recently, solving CO problems on graphs…
Supervised Contrastive Learning with Structure Inference for Graph Classification
Hao Jia, Junzhong Ji, Minglong Lei
Advanced graph neural networks have shown great potentials in graph classification tasks recently. Different from node classification where node embeddings aggregated from local ne…
Multi-task Self-distillation for Graph-based Semi-Supervised Learning
Yating Ren, Junzhong Ji, Lingfeng Niu +1
Graph convolutional networks have made great progress in graph-based semi-supervised learning. Existing methods mainly assume that nodes connected by graph edges are prone to have…
Latent Network Embedding via Adversarial Auto-encoders
Minglong Lei, Yong Shi, Lingfeng Niu
Graph auto-encoders have proved to be useful in network embedding task. However, current models only consider explicit structures and fail to explore the informative latent structu…
Diffusion Based Network Embedding
Yong Shi, Minglong Lei, Peng Zhang +1
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk m…