4 papers
Scale-aware Message Passing For Graph Node Classification
Qin Jiang, Chengjia Wang, Michael Lones +2
Most Graph Neural Networks (GNNs) operate at the first-order scale, even though multi-scale representations are known to be crucial in domains such as image classification. In this…
Position: Spectral GNNs Are Neither Spectral Nor Superior for Node Classification
Qin Jiang, Chengjia Wang, Michael Lones +2
Spectral Graph Neural Networks (Spectral GNNs) for node classification promise frequency-domain filtering on graphs, yet rest on flawed foundations. Recent work shows that graph La…
Demystifying MPNNs: Message Passing as Merely Efficient Matrix Multiplication
Qin Jiang, Chengjia Wang, Michael Lones +1
While Graph Neural Networks (GNNs) have achieved remarkable success, their design largely relies on empirical intuition rather than theoretical understanding. In this paper, we pre…
ScaleNet: Scale Invariance Learning in Directed Graphs
Qin Jiang, Chengjia Wang, Michael Lones +2
Graph Neural Networks (GNNs) have advanced relational data analysis but lack invariance learning techniques common in image classification. In node classification with GNNs, it is…