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Found 10 papers, 2 papers with code
GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection
To our knowledge, we are the first to shed light on the connection between the system model and graph convolution, and the first to design the data-dependent attention scores for graph convolution.
Representation Learning on Heterophilic Graph with Directional Neighborhood Attention
However, since it only incorporates information from immediate neighborhood, it lacks the ability to capture long-range and global graph information, leading to unsatisfactory performance on some datasets, particularly on heterophilic graphs.
Bidirectional Generative Pre-training for Improving Time Series Representation Learning
Learning time-series representations for discriminative tasks has been a long-standing challenge.
Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting
This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale data and ability to capture long-term temporal dependencies.
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Homophily principle, i. e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks.
When Do We Need Graph Neural Networks for Node Classification?
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i. i. d.)
Revisiting Heterophily For Graph Neural Networks
ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers.
Inductive Bias
Node Classification on Non-Homophilic (Heterophilic) Graphs
Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Ranked #1 on
Node Classification
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Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
