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Found 13 papers, 6 papers with code
Simplifying Graph Transformers
Researchers have attempted to migrate Transformers to graph learning, but most advanced Graph Transformers are designed with major architectural differences, either integrating message-passing or incorporating sophisticated attention mechanisms.
Sparse Decomposition of Graph Neural Networks
Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a memory fetch for a very large number of nodes.
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains.
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
In this survey, we provide a comprehensive review of the latest progress on heterophilic graph learning, including an extensive summary of benchmark datasets and evaluation of homophily metrics on synthetic graphs, meticulous classification of the most updated supervised and unsupervised learning methods, thorough digestion of the theoretical analysis on homophily/heterophily, and broad exploration of the heterophily-related applications.
CKGConv: General Graph Convolution with Continuous Kernels
In this work, we propose a novel and general graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding.
Ranked #1 on
Graph Classification
on CIFAR-10
Revealing Decurve Flows for Generalized Graph Propagation
\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.
Multi-resolution Time-Series Transformer for Long-term Forecasting
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
Graph Inductive Biases in Transformers without Message Passing
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 CLUSTER
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
Graph Attention Networks with Positional Embeddings
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.
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
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.
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
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.
Memory Augmented Graph Neural Networks for Sequential Recommendation
In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.
