REVIEW 4 cited by
Directed Graph Convolutional Network
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data. However, the undirected graphs limit their application scope. In this paper, we extend spectral-based graph convolution to directed graphs by using first- and second-order proximity, which can not only retain the connection properties of the directed graph, but also expand the receptive field of the convolution operation. A new GCN model, called DGCN, is then designed to learn representations on the directed graph, leveraging both the first- and second-order proximity information. We empirically show the fact that GCNs working only with DGCNs can encode more useful information from graph and help achieve better performance when generalized to other models. Moreover, extensive experiments on citation networks and co-purchase datasets demonstrate the superiority of our model against the state-of-the-art methods.
Forward citations
Cited by 4 Pith papers
-
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
A hybrid small-world graph plus flow-regime encoder yields more accurate, robust, and sample-efficient multi-agent trajectory forecasts than strong data-driven baselines on nuScenes, MoCAD and NGSIM.
-
AI Generalisation Gap In Comorbid Sleep Disorder Staging
EEG sleep-staging models that work on healthy subjects generalize poorly to ischemic stroke patients and attend to physiologically uninformative signal regions.
-
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
S2-DiGCL combines personalized magnetic Laplacian perturbations with BFS/DFS path sampling to improve contrastive learning on directed graphs.
-
Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs
A GAT model with polynomial gating (Poly) and a directed variant (Dir-Poly) report strong heterophilic node classification results, with Dir-Poly's largest gain on a single directed dataset.
Discussion (0). Sign in to comment.