25 citations · 60 across the 4 of their papers we have counts for
4 papers
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
Yunsheng Bai, Hao Ding, Yang Qiao +5
We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-…
Convolutional Set Matching for Graph Similarity
Yunsheng Bai, Hao Ding, Yizhou Sun +1
We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs. As the core operation of graph similar…
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
Yunsheng Bai, Hao Ding, Yizhou Sun +1
Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since…
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Yunsheng Bai, Hao Ding, Song Bian +3
Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity comput…