1 code implementation • 24 Nov 2023 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen, Yusu Wang
To efficiently encode the space of all cycles, we start with a cycle basis (i. e., a minimal set of cycles generating the cycle space) which we compute via the kernel of the 1-dimensional Hodge Laplacian of the input graph.
1 code implementation • 19 Mar 2023 • Zuoyu Yan, Junru Zhou, Liangcai Gao, Zhi Tang, Muhan Zhang
Among these works, a popular way is to use subgraph GNNs, which decompose the input graph into a collection of subgraphs and enhance the representation of the graph by applying GNN to individual subgraphs.
1 code implementation • 28 Jan 2022 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen
Topological features based on persistent homology capture high-order structural information so as to augment graph neural network methods.
1 code implementation • 6 Oct 2021 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
In this paper, we consider rules as cycles and show that the space of cycles has a unique structure based on the mathematics of algebraic topology.
Graph Representation Learning Inductive Relation Prediction +1
no code implementations • 29 Sep 2021 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
We propose to collect cycle bases that span the space of cycles.
2 code implementations • 6 May 2021 • Wenqi Zhao, Liangcai Gao, Zuoyu Yan, Shuai Peng, Lin Du, Ziyin Zhang
Encoder-decoder models have made great progress on handwritten mathematical expression recognition recently.
no code implementations • 24 Apr 2021 • Ke Yuan, Zuoyu Yan, Yibo Li, Liangcai Gao, Zhi Tang
In the Selector, a Topic Relation Graph (TRG) is proposed to obtain the relevant documents which contain the comprehensive information of math expressions.
1 code implementation • 20 Feb 2021 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
Link prediction is an important learning task for graph-structured data.
no code implementations • 23 Dec 2020 • Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, Zhi Tang
Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout.