Cooperative Causal GraphSAGE
Zaifa Xue, Tao Zhang, Tuo Xu, Huaixin Liang, Le Gao

TL;DR
This paper introduces Cooperative Causal GraphSAGE, a novel method combining cooperative game theory with causal graph neural networks to improve robustness and stability in node embedding and classification tasks.
Contribution
It proposes a cooperative causal structure model and CoCa-sampling algorithm using Shapley values to enhance neighborhood sampling in causal graph neural networks.
Findings
Achieves comparable classification accuracy to existing methods.
Outperforms others under data perturbations.
Demonstrates improved robustness and stability.
Abstract
GraphSAGE is a widely used graph neural network. The introduction of causal inference has improved its robust performance and named as Causal GraphSAGE. However, Causal GraphSAGE focuses on measuring causal weighting among individual nodes, but neglecting the cooperative relationships among sampling nodes as a whole. To address this issue, this paper proposes Cooperative Causal GraphSAGE (CoCa-GraphSAGE), which combines cooperative game theory with Causal GraphSAGE. Initially, a cooperative causal structure model is constructed in the case of cooperation based on the graph structure. Subsequently, Cooperative Causal sampling (CoCa-sampling) algorithm is proposed, employing the Shapley values to calculate the cooperative contribution based on causal weights of the nodes sets. CoCa-sampling guides the selection of nodes with significant cooperative causal effects during the neighborhood…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Data-Driven Disease Surveillance
MethodsCausal inference · GraphSAGE
