no code implementations • 21 Apr 2024 • Liheng Ma, Soumyasundar Pal, Yitian Zhang, Jiaming Zhou, Yingxue Zhang, Mark Coates
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified.
no code implementations • 13 Feb 2024 • Chen Lin, Liheng Ma, Yiyang Chen, Wanli Ouyang, Michael M. Bronstein, Philip H. S. Torr
\textbf{Secondly}, we propose the {\em Continuous Unified Ricci Curvature} (\textbf{CURC}), an extension of celebrated {\em Ollivier-Ricci Curvature} for directed and weighted graphs.
2 code implementations • 7 Nov 2023 • Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
1 code implementation • 27 May 2023 • Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip Torr, Ser-Nam Lim
Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.
Ranked #1 on Node Classification on PATTERN
1 code implementation • 10 Jun 2021 • Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
no code implementations • 9 May 2021 • Liheng Ma, Reihaneh Rabbany, Adriana Romero-Soriano
In this framework, the positional embeddings are learned by a model predictive of the graph context, plugged into an enhanced GAT architecture, which is able to leverage both the positional and content information of each node.
no code implementations • 13 Jan 2021 • Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.
no code implementations • 13 Jan 2021 • Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.
1 code implementation • 26 Dec 2019 • Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates
In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.