fix dominant num_layers

README.rst

@@ -77,19 +77,25 @@ See examples below for detecting outliers with PyGOD in 5 lines!
 
 **Citing PyGOD**\ :
 
-Our `PyGOD benchmark paper <https://arxiv.org/abs/2206.10071>`_ is available on arxiv.
-If you use PyGOD in a scientific publication, we would appreciate
-citations to the following paper::
-
-    @article{liu2022benchmarking,
+Our `software paper <https://arxiv.org/abs/2204.12095>`_ and `benchmark paper <https://arxiv.org/abs/2206.10071>`_ are available on arxiv.
+If you use PyGOD in a scientific publication, we would appreciate citations to the following papers::
+
+    @article{liu2022pygod,
+      title={PyGOD: A Python Library for Graph Outlier Detection},
+      author={Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
+      journal={arXiv preprint arXiv:2204.12095},
+      year={2022}
+    }
+    @article{liu2022bond,
       author  = {Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Sun, Lichao and Li, Jundong and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
-      title   = {Benchmarking Node Outlier Detection on Graphs},
+      title   = {BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs},
       journal = {arXiv preprint arXiv:2206.10071},
       year    = {2022},
     }
 
 or::
 
+    Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K. and Chen, G.H., Jia, Z., and Yu, P.S. 2022. PyGOD: A Python Library for Graph Outlier Detection. arXiv preprint arXiv:2204.12095.
     Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K., Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S. 2022. Benchmarking Node Outlier Detection on Graphs. arXiv preprint arXiv:2206.10071.
 
 

docs/index.rst

@@ -86,19 +86,25 @@ See examples below for detecting anomalies with PyGOD in 5 lines!
 
 **Citing PyGOD**\ :
 
-Our `PyGOD benchmark paper <https://arxiv.org/abs/2206.10071>`_ is available on arxiv :cite:`liu2022benchmarking`.
-If you use PyGOD in a scientific publication, we would appreciate
-citations to the following paper::
-
-    @article{liu2022benchmarking,
+Our `software paper <https://arxiv.org/abs/2204.12095>`_ and `benchmark paper <https://arxiv.org/abs/2206.10071>`_ are available on arxiv.
+If you use PyGOD in a scientific publication, we would appreciate citations to the following papers::
+
+    @article{liu2022pygod,
+      title={PyGOD: A Python Library for Graph Outlier Detection},
+      author={Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
+      journal={arXiv preprint arXiv:2204.12095},
+      year={2022}
+    }
+    @article{liu2022bond,
       author  = {Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Sun, Lichao and Li, Jundong and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
-      title   = {Benchmarking Node Outlier Detection on Graphs},
+      title   = {BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs},
       journal = {arXiv preprint arXiv:2206.10071},
       year    = {2022},
     }
 
 or::
 
+    Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K. and Chen, G.H., Jia, Z., and Yu, P.S. 2022. PyGOD: A Python Library for Graph Outlier Detection. arXiv preprint arXiv:2204.12095.
     Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K., Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S. 2022. Benchmarking Node Outlier Detection on Graphs. arXiv preprint arXiv:2206.10071.
 
 

pygod/models/dominant.py

@@ -33,8 +33,8 @@ class DOMINANT(BaseDetector):
     hid_dim :  int, optional
         Hidden dimension of model. Default: ``0``.
     num_layers : int, optional
-        Total number of layers in model. A half (ceil) of the layers
-        are for the encoder, the other half (floor) of the layers are
+        Total number of layers in model. A half (floor) of the layers
+        are for the encoder, the other half (ceil) of the layers are
         for decoders. Default: ``4``.
     dropout : float, optional
         Dropout rate. Default: ``0.``.
@@ -275,8 +275,8 @@ def __init__(self,
         super(DOMINANT_Base, self).__init__()
 
         # split the number of layers for the encoder and decoders
-        decoder_layers = int(num_layers / 2)
-        encoder_layers = num_layers - decoder_layers
+        encoder_layers = int(num_layers / 2)
+        decoder_layers = num_layers - encoder_layers
 
         self.shared_encoder = GCN(in_channels=in_dim,
                                   hidden_channels=hid_dim,