2 code implementations • 8 Nov 2018 • Chen Cai, Yusu Wang
We test our baseline representation for the graph classification task on a range of graph datasets.
Ranked #22 on Graph Classification on MUTAG
1 code implementation • ICLR 2022 • Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture.
1 code implementation • 28 Jan 2022 • Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation.
1 code implementation • 31 Mar 2022 • Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman, Nina Wiedemann, Martin Raubal, Bo wang, Hai L. Vu, Reza Mohajerpoor, Chen Cai, Inhi Kim, Luca Hermes, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Jay Santokhi, Dylan Hillier, Yiming Yang, Joned Sarwar, Anna Jordan, Emil Hewage, David Jonietz, Fei Tang, Aleksandra Gruca, Michael Kopp, David Kreil, Sepp Hochreiter
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
1 code implementation • 23 Jun 2020 • Chen Cai, Yusu Wang
In this paper, we build upon previous results \cite{oono2019graph} to further analyze the over-smoothing effect in the general graph neural network architecture.
1 code implementation • 27 Jan 2023 • Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang
Graph Transformer (GT) recently has emerged as a new paradigm of graph learning algorithms, outperforming the previously popular Message Passing Neural Network (MPNN) on multiple benchmarks.
Ranked #8 on Node Classification on PascalVOC-SP
1 code implementation • 11 Jun 2023 • Chen Cai
This thesis presents a local-to-global perspective on graph neural networks (GNN), the leading architecture to process graph-structured data.
1 code implementation • ICLR 2021 • Chen Cai, Dingkang Wang, Yusu Wang
As large-scale graphs become increasingly more prevalent, it poses significant computational challenges to process, extract and analyze large graph data.
1 code implementation • 2 May 2019 • Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan, Goonmeet Bajaj, Chen Cai, Moniba Keymanesh, Saravana Kumar, Pranav Maneriker, Anasua Mitra, Vedang Patel, Balaraman Ravindran, Srinivasan Parthasarathy
An important area of research that has emerged over the last decade is the use of graphs as a vehicle for non-linear dimensionality reduction in a manner akin to previous efforts based on manifold learning with uses for downstream database processing, machine learning and visualization.
1 code implementation • 10 Nov 2021 • Bo wang, Reza Mohajerpoor, Chen Cai, Inhi Kim, Hai L. Vu
The aim is to build a machine learning model for predicting the normalized average traffic speed and flow of the subregions of multiple large-scale cities using historical data points.
no code implementations • 29 May 2019 • Adriana-Simona Mihaita, Zheyuan Liu, Chen Cai, Marian-Andrei Rizoiu
Predicting traffic incident duration is a major challenge for many traffic centres around the world.
no code implementations • 11 Jun 2019 • Sajjad Shafiei, Adriana-Simona Mihaita, Chen Cai
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model.
no code implementations • 11 Jun 2019 • Tuo Mao, Adriana-Simona Mihaita, Chen Cai
Secondly, we apply the optimal signal timings previously found under severe incidents affecting the traffic flow in the network but without any further optimization.
no code implementations • 11 Sep 2019 • Chen Cai
Knowledge graph embedding has recently become a popular way to model relations and infer missing links.
no code implementations • 16 Jan 2020 • Chen Cai, Yusu Wang
For shape segmentation and classification, however, we note that persistence pairing shows significant power on most of the benchmark datasets, and improves over both summaries based on merely critical values, and those based on permutation tests.
no code implementations • 11 Nov 2020 • Dilusha Weeraddana, Nguyen Lu Dang Khoa, Lachlan O Neil, Weihong Wang, Chen Cai
Given these considerations, we construct a stacked GP method where the predictive posteriors of each GP applied to each task are used in the prior and likelihood of the next level GP.
no code implementations • 12 Apr 2021 • Chen Cai, Nikolaos Vlassis, Lucas Magee, Ran Ma, Zeyu Xiong, Bahador Bahmani, Teng-Fong Wong, Yusu Wang, WaiChing Sun
Comparisons among predictions inferred from training the CNN and those from graph convolutional neural networks (GNN) with and without the equivariant constraint indicate that the equivariant graph neural network seems to perform better than the CNN and GNN without enforcing equivariant constraints.
no code implementations • ICLR Workshop GTRL 2021 • Chen Cai
To better understand the power and limitation of persistence diagrams, we carry out a range of experiments on both graph and shape data, aiming to decouple and inspect the effects of different factors involved.
no code implementations • 25 Jan 2022 • Chen Cai, Yusu Wang
Building upon this result, we prove the convergence of $k$-IGN under the model of \citet{ruiz2020graphon}, where we access the edge weight but the convergence error is measured for graphon inputs.
no code implementations • 13 Jun 2023 • Chen Cai, Suchen Wang, Kim-Hui Yap, Yi Wang
Weakly-supervised grounded image captioning (WSGIC) aims to generate the caption and ground (localize) predicted object words in the input image without using bounding box supervision.