全能 发表于 2025-3-30 09:51:52
,Maximal Independent Vertex Set Applied to Graph Pooling,ing a Maximal Independent Vertex Set (MIVS) and an assignment of the remaining vertices to the survivors. Consequently, our method does not discard any vertex information nor artificially increase the density of the graph. Experimental results show an increase in accuracy for graph classification on various standard datasets.Deject 发表于 2025-3-30 14:44:07
http://reply.papertrans.cn/89/8801/880083/880083_52.pngStricture 发表于 2025-3-30 17:06:44
http://reply.papertrans.cn/89/8801/880083/880083_53.pnganaphylaxis 发表于 2025-3-30 23:38:48
,Graph Reduction Neural Networks for Structural Pattern Recognition,k. Eventually, we use the reduced graphs in conjunction with graph edit distance and a distance-based classifier. On five datasets we empirically confirm the benefit of the novel reduction scheme regarding both classification performance and computation time.Foam-Cells 发表于 2025-3-31 01:18:36
http://reply.papertrans.cn/89/8801/880083/880083_55.pngexceptional 发表于 2025-3-31 07:42:52
http://reply.papertrans.cn/89/8801/880083/880083_56.png巡回 发表于 2025-3-31 10:36:03
http://reply.papertrans.cn/89/8801/880083/880083_57.pngcruise 发表于 2025-3-31 14:15:18
http://reply.papertrans.cn/89/8801/880083/880083_58.png蛰伏 发表于 2025-3-31 20:02:52
,A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures,f the node feature (if available) and we define a kernel between two input graphs in terms of the Wasserstein distance between the respective node embeddings. Experiments on standard graph classification benchmarks show that our kernel performs favourably when compared to widely used alternative kernels as well as graph neural networks.窝转脊椎动物 发表于 2025-3-31 21:52:42
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