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[image] neurips logo pdf. 图片1. Advances in Neural Information Processing Systems (NeurIPS), 2024.

Backgrounds of Multiplex Graph. ❖ What is multiplex graph？.

Backgrounds of Multiplex Graph. ❖ Overlooked Aspects of Unsupervised Multiplex Graph Learning.

Motivation. Definition 1. Gi is considered non-redundant with Gj for Y if and only if there exists € > 0 such that the conditional mutual information I (Gt; Y I G)) > € or I Y I Gt) > €..

Motivation. Definition 1. Gi is considered non-redundant with Gj for Y if and only if there exists € > 0 such that the conditional mutual information I (Gt; Y I G)) > € or I Y I Gt) > €..

Methodology. Multiplex Graph Graph Structure Refinement Task-relevant Information Maximizing GI Graph Learner Optimal Graph Aug. Refined Graph Fused Graph Refined Graph Optimal Graph Aug. Shared Graph Encoder Update : Parameters Maximize Mutual Information 'Downstream Node Classification Node Clustering Tasks Unique Task- relevant Information Shared Task- relevant Information Graph Fusion.

Theoretical Contributions. ❖ Optimal Graph Augmentation.

Experiments. Table l: Quantitative results (%) on node clustering. The top 3 highest results are highlighted with red boldface, red color and boldface, respectively. The symbol "00M" means out of memory. ACM DBLP Method VGAE DGl 02MAC MvAGC MCGC HDMI MGDCR DMG BTGF InfoMGF-RA InfoMGF-LA Yelp NMI 45.83 52.94 42.36 64.49 60.21 65.44 58.8 64.14 68.92 74.89 76.53 AR1 41.36 47.55 46.04 66.81 50.72 68.87 55.15 67.21 73.14 81.09 81.49 ACC 67.93 65.36 77.92 87.17 65.62 88.11 73.82 87.11 90.09 92.82 93.45 Fl 68.62 57.34 78.01 87.21 54.78 88.14 70.34 87.23 90.11 92.89 93.42 NMI 61.79 65.59 58.64 50.39 65.56 64.85 62.47 69.03 66.28 70.19 73.22 AR1 65.56 70.35 60.01 51.21 71.51 70.85 62.22 73.07 72.47 73.49 78.49 ACC 84.48 86.88 83.29 78.39 87.96 87.39 81.91 88.45 88.05 88.72 91.08 83.67 86.02 82.88 77.84 87.47 86.75 80.16 87.88 87.28 88.31 90.69 NMI 39.19 39.42 39.02 24.39 38.35 60.81 44.23 65.66 69.97 72.67 75.18 AR1 42.57 42.62 42.53 29.25 35.17 59.35 46.47 66.33 73.53 74.66 78.91 ACC 65.07 65.29 65.07 63.14 65.61 79.56 72.71 88.26 91.39 91.85 93.26 Fl 56.74 56.79 56.74 56.7 57.49 77.6 54.43 89.27 92.32 92.86 94.01 NMI 53.56 48.15 54.43 48.72 56.65 MAG AR1 ACC OOM 42.6 59.89 OOM 00M OOM 34.92 51.78 43.98 61.37 39.77 61.61 OOM 45.25 64.13 00M Fl 57.17 49.8 60.53 60.16 63.09 Table 2: Quantitative results with standard deviation (% ± a) on node classification. Available data for GSL during training is shown in the first column, supervised methods depend on Y for GSL. The symbol " " indicates that the method is structure-fixed, which does not learn a new structure. Available Data for GSI- x,Y,A x,Y,A x,A x,A x,A x,A x,A Methods GCN GAT HAN I-DS GRCN IDGI- ProGNN GEN NodeFormer SUBLIME STABLE GSR HDMI DMG BTGF InfoMGF-RA InfoMGF-LA ACM DBLP Yelp Macro-Fl 90.27±0.59 91.52±0.62 91.67±0.39 92.35±0.43 93.04±0.17 91.69±1.24 90.57±1.03 87.91±2.78 91.33±0.77 92.42±0.16 83.54±4.20 92.14±1.08 91.01±0.32 90.42±0.36 91.75±0.11 93.21±0.22 93.42±0.21 Micro-Fl 90.18±0.61 91.46±0.62 91.47±0.22 92.05±0.26 92.94±0.18 91.63* I .24 90.50±1.29 87.88±2.61 90.60±0.95 92.13±0.37 83.38±4.51 92.11±0.99 90.86±0.31 90.31+0.35 91.62±0.11 93.14±0.21 9335±0.21 Macro-Fl 90.01±0.32 90.22±0.37 90.53±0.24 88.11±0.86 88.33±0.47 89.65±0.60 83.13±1.56 89.74±0.69 79.54±0.78 90.98±0.37 75.18±1.95 76.59±0.45 89.91±0.49 90.42+0.57 90.71±0.24 90.99±0.36 91.28±0.31 Micro-Fl 9099±0.28 91.13±0.40 91.47±0.22 88.74±0.85 89.43±0.44 90.61±0.56 84.83±1.36 90.65±0.71 80.56±0.62 91.82±0.27 76.42±1.95 77.69±0.42 90.89±0.51 91.34+0.49 91.57±0.21 91.93±0.29 92.12±0.28 Macro-Fl 78.01±1.89 82.12±1.47 88.49±1.73 75.98±2.35 76.05±1.05 76.98±5.78 51.76±1.46 80.43±3.78 91.69±0.65 79.68±0.79 71.48±4.71 83.85±0.76 80.73±0.64 91.61+0.62 92.81±1.12 93.09±0.27 93.26±0.26 Micro-Fl 81.03±1.81 84.43±1.56 88.78±1.40 78.14±1.98 80.68±0.96 79.15±5.06 58.39±1.25 82.68±2.84 90.59±1.21 82.99±0.82 76.62±2.75 85.73±0.54 84.05±0.91 90.24±0.81 9137±1.28 92.02±0.34 92.24±0.34 MAG Macro-Fl Micro-Fl 75.98±0.07 75.76±0.10 OOM 00M 00M OOM 00M OOM 00M 77.21±0.18 77.08±0.19 75.96±0.05 75.71±0.03 00M OOM 72.22±0.14 71.84±0.15 76.34+0.09 76.13±0.10 OOM 77.25±0.06 77.11±0.06 OOM.

Experiments. Cl (a) PAP (b) PSP Figure 3: Heatmaps of the subgraph adjacency matrices of the original and learned graphs on ACM..

Summary of InfoMGF. ❖ Key takeaways: · GSL perspective: Explore graph structure learning in heterogeneous multiplex graph through a data-centric paradigm. · Beyond redundancy: Emphasize the importance of unique task-relevant information to better adapt to real- world non-redundant scenarios..