Review Article
Survey of Graph Neural Networks and Applications
Table 2
Summary of existing spectral convolution approaches.
| Convolution schemes | Task | Input parameters | Input data | No. of layers | PERF | Time complexity |
| Spectral CNN [38] | Classification | | MNIST | — | 98.7% | | SyncSpecCNN [47] | Identification | | Annotations | 10 | 84.74% | | SyncSpecCNN [48] | Prediction | | DBLQ network data | — | — | — | SyncSpecCNN [49] | Classification | | Yeast dataset | — | 56.0% | — | SSF-CNN [50] | Classification | | HS, CAVE | 3 | — | | Graph CNN [51] | Identification | | MNIST | — | 94.23% | | GCN [52] | Prediction | | MINI | 2 | 77% | | Semisupervised GCN [41] | Classification | | Cora, CiteSeer | 2 | 70.3% | | GCN [53] | Classification | | Protein data | — | 84.6% | — | Multigraph GCN [54] | Classification | | Bunny mesh | — | 93.58% | — | Local SGCN [55] | Classification | | MNIST | 2 | 95.74% | | S-GCN [56] | Classification | | Reddit, Flickr | — | 96.8% | | S-GCN [57] | Classification | | Cora, CiteSeer | 6 | 83.12% | | DSGCN [18] | Classification | | ENZYMES | 7 | 78.39% | | Semisupervised GCN [58] | Classification | | Cora, CiteSeer | 2 | 74.5% | — | AGCN [59] | Prediction | | Delaney 2004 | 7 | 79.4% | | Multiscale GCN [60] | Classification | | Cora, CiteSeer | — | 79.2% | | EGCN [61] | Classification | | RF, Weave | 2 | 82.0% | | Functional brain network [62] | Classification | | ABIDE | 10 | 90.0% | | Text level GCN [63] | Classification | | R52 | — | 94.6% | — | Edge-labeling GCN [64] | Classification | | miniImageNet | — | 76.4% | | Graph WNN [65] | Classification | | Cora, CiteSeer | 16 | 82.8% | | GNN [66] | Classification | | HDM05, LSC | 5 | 98.6% | — | StemGNN [67] | Identification | | COVID-19 | — | 91.74% | | PA-GNN [68] | Defense malicious attack | | Reddit | 2 | 79.57% | | Quantum GNN [69] | Classification | | Kolmogorov-Smirnoff | — | 95.3% | | Recurrent multi-GNN [70] | Classification | | Synthetic dataset | — | 99.3% | | Few-shot GNN [71] | Classification | | ILSVRC-12 | 5 | 99.2% | | Transferability GNN [72] | Classification | | Cora | — | 76.5% | | Line-GNN [73] | Classification | | Stochastic block mode | 30 | 93.7% | | Spectrum DNN [74] | Classification | | UMD Wikipedia dataset | — | 82.61% | | Spectral marching [75] | Identification | | Image | — | 96.1% | | TGC-LSTM [76] | Prediction | | INRIX traffic | 11 | 97.43% | | Graph-ARMA [77] | Classification | | MNIST, 20news | 3 | 91.5% | | GCF [78] | Prediction | | ML-10M, Taobao | 3 | 79.6% | — | Spectral clustering [19] | Classification | | Cora, CiteSeer | — | 98.7% | | LB spectral filtering [79] | Classification | | ADNI | 5 | 91.1% | — | GSDN-F, GSDN-EF [80] | Classification | | Cora, CiteSeer | — | 95.7% | | DAGN [81] | Classification | | Cora, CiteSeer | 24 | 85.4% | | Graph hashing network [82] | Classification | | MNIST | 6 | 84.2% | | KM2A arrays [83] | Classification | | Cosmic-ray data | — | 95.3% | | CayleyNets [84] | Classification | | MNIST | 2 | 99.18% | | Median spectral graph [85] | Identification | | Attributed graphs | — | 83.2% | | GfNN [86] | Classification | | Cora, CiteSeer | 2 | 80.9% | | GIN [87] | Classification | | MUTAG | — | 91.6% | — | LNPP/SBMF [88] | Classification | | Social network | — | 96.8% | — | Learning graph [89] | Classification | | Chemical molecular dataset | 2 | 86.4% | | LNN with graph sparsity [90] | Classification | | MNIST | 5 | 96.1% | — | GeniePath [91] | Classification | | MINI | 18 | 96.5% | | Heterogeneous GAN [92] | Classification | | DBLP, ACM | — | 84.76% | |
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