update reference and test

pygod-team · Dec 19, 2023 · 4db367c · 4db367c
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diff --git a/README.rst b/README.rst
@@ -181,6 +181,7 @@ OCGNN               2021   GNN+AE       Yes          [#Wang2021One]_
 CoLA                2021   GNN+AE+SSL   Yes          [#Liu2021Anomaly]_
 GUIDE               2021   GNN+AE       Yes          [#Yuan2021Higher]_
 CONAD               2022   GNN+AE+SSL   Yes          [#Xu2022Contrastive]_
+GADNR               2023   GNN+AE       Yes          [#Roy2023Gadnr]_
 ==================  =====  ===========  ===========  ========================================
 
 
@@ -256,3 +257,5 @@ Reference
 .. [#Yuan2021Higher] Yuan, X., Zhou, N., Yu, S., Huang, H., Chen, Z. and Xia, F., 2021, December. Higher-order Structure Based Anomaly Detection on Attributed Networks. In 2021 IEEE International Conference on Big Data (Big Data).
 
 .. [#Xu2022Contrastive] Xu, Z., Huang, X., Zhao, Y., Dong, Y., and Li, J., 2022. Contrastive Attributed Network Anomaly Detection with Data Augmentation. In Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
+
+.. [#Roy2023Gadnr] Roy, A., Shu, J., Li, J., Yang, C., Elshocht, O., Smeets, J. and Li, P., 2023. GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM).
diff --git a/docs/index.rst b/docs/index.rst
@@ -104,6 +104,7 @@ OCGNN               2021   GNN+AE       Yes          :class:`pygod.detector.OCGN
 CoLA                2021   GNN+AE+SSL   Yes          :class:`pygod.detector.CoLA`
 GUIDE               2021   GNN+AE       Yes          :class:`pygod.detector.GUIDE`
 CONAD               2022   GNN+AE+SSL   Yes          :class:`pygod.detector.CONAD`
+GADNR               2023   GNN+AE       Yes          :class:`pygod.detector.GADNR`
 ==================  =====  ===========  ===========  ==============================================
 
 

diff --git a/docs/zreferences.bib b/docs/zreferences.bib
@@ -171,4 +171,11 @@ @article{kipf2016variational
   author={Kipf, Thomas N and Welling, Max},
   journal={arXiv preprint arXiv:1611.07308},
   year={2016}
+}
+
+@inproceedings{roy2023gadnr,
+  title  = {GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction},
+  author = {Roy, Amit and Shu, Juan and Li, Jia and Yang, Carl and Elshocht, Olivier and Smeets, Jeroen and Li, Pan},
+  booktitle={Proceedings of the 17th ACM International Conference on Web Search and Data Mining},
+  year   = {2024}
 }
diff --git a/pygod/test/test_gadnr.py b/pygod/test/test_gadnr.py
@@ -2,11 +2,10 @@
 import os
 import unittest
 from numpy.testing import assert_equal
-from numpy.testing import assert_warns
 from numpy.testing import assert_raises
 
 import torch
-from torch_geometric.nn import GIN, GCN
+from torch_geometric.nn import GCN
 from torch_geometric.seed import seed_everything
 
 from pygod.metric import eval_roc_auc
@@ -83,12 +82,11 @@ def test_sample(self):
                         num_neigh=1,
                         contamination=0.2,
                         verbose=3,
-                        save_emb=True,
-                        act_first=True)
+                        save_emb=True)
         detector.fit(self.train_data)
 
         score = detector.predict(return_pred=False, return_score=True)
-        # TODO: assert (eval_roc_auc(self.train_data.y, score) >= self.roc_floor)
+        assert (eval_roc_auc(self.train_data.y, score) >= self.roc_floor)
 
         pred, score, conf, emb = detector.predict(self.test_data,
                                                   return_pred=True,
@@ -97,7 +95,7 @@ def test_sample(self):
                                                   return_emb=True)
 
         assert_equal(pred.shape[0], self.test_data.y.shape[0])
-        # TODO: assert (eval_roc_auc(self.test_data.y, score) >= self.roc_floor)
+        assert (eval_roc_auc(self.test_data.y, score) >= self.roc_floor)
         assert_equal(conf.shape[0], self.test_data.y.shape[0])
         assert (conf.min() >= 0)
         assert (conf.max() <= 1)
@@ -124,8 +122,3 @@ def test_sample(self):
             detector.predict(self.test_data,
                              return_prob=True,
                              prob_method='something')
-
-    def test_params(self):
-        with assert_warns(UserWarning):
-            detector = GADNR(num_neigh=1, backbone=GIN)
-            detector.fit(self.test_data)