Proposes time-ordered splitting to avoid leakage, the Taoke e-commerce cascade dataset with conversion signals, and the lightweight CasTemp model using temporal walks and attention for efficient SOTA cascade popularity prediction.
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3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
IDP-DSN separates positive and negative dynamics in signed networks using dedicated memories and disentangles static and dynamic features to boost inductive edge prediction performance.
citing papers explorer
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Beyond Leakage and Complexity: Towards Realistic and Efficient Information Cascade Prediction
Proposes time-ordered splitting to avoid leakage, the Taoke e-commerce cascade dataset with conversion signals, and the lightweight CasTemp model using temporal walks and attention for efficient SOTA cascade popularity prediction.
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Graph Retention Networks for Dynamic Graphs
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
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Inductive Dual-Polarity Modeling via Static-Dynamic Disentanglement for Dynamic Signed Networks
IDP-DSN separates positive and negative dynamics in signed networks using dedicated memories and disentangles static and dynamic features to boost inductive edge prediction performance.