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| Name | KDD 2023 - Learning Strong Graph Neural Networks with Weak Information |
| Video Uploader | Video From Association for Computing Machinery (ACM) |
| Upload Date | This Video Uploaded At 10-07-2023 06:00:02 |
| Video Discription | Yixin Liu, Monash University This is a short promo video to introduce our paper ?Learning Strong Graph Neural Networks with Weak Information?. In this paper, we aim to investigate a new research problem of Graph learning with weak information (GLWI). We consider the graph learning problem with weak structure, weak features, weak labels, and an extreme scenario where all data elements are deficient. Our target is to design an effective GNN to address GLWI problem. Following the question, we first find out that long-range information propagation plays a crucial role in mitigating incomplete data. That is, the long?range information propagation in GNNs can benefit feature completion, supervision signal propagation, and structure leverage. Then, for the extreme GLWI scenario, we find the scattered nodes isolated from the largest connected component (i.e., stray nodes) lead to ineffective information propagation, which severely hurts the feature completion and signal propagation in GNN. Finally, following the criteria of long-range propagation and handling stray node problem, we propose a novel GNN termed Dual-channel Diffused Propagation then Transformation (D2PT) for GLWI problem. Our experiments show that our proposed method significantly outperforms the existing baselines in various GLWI scenarios. For more details please check out our paper! |
| Category | Science & Technology |
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