add GCNJaccard/GCNSVD

autogl/module/preprocessing/structure_engineering/_structure_engineer.py

@@ -5,17 +5,104 @@ class StructureEngineer(_data_preprocessor.DataPreprocessor):
     ...
 
 
+
+import torch
+from ....utils import get_logger
+LOGGER = get_logger("Structure")
+
+from torch_geometric.utils import to_dense_adj
+def get_feature(data):
+    """return features : numpy.ndarray
+    """
+    for fk in 'x feat'.split():
+        if fk in data.nodes.data:
+            features=data.nodes.data[fk].numpy()
+    return features
+
+def get_edges(data):
+    return data.edges.connections
+
+def set_edges(data,adj):
+    data.data["edge_index"]=adj
+
+def to_adjacency_matrix(adj):
+    """
+    adj : torch.Tensor [2,E]
+    return Tensor [N,N]
+    """
+    adj = to_dense_adj(adj)[0].long() # adjacency matrix
+    return adj
+def to_adjacency_list(adj):
+    """
+    adj : Tensor [N,N]
+    return Tensor [2,E]
+    """
+    adj = torch.stack(adj.nonzero(as_tuple=True)).long() # edge list 
+    return adj
+
 from .._data_preprocessor_registry import DataPreprocessorUniversalRegistry
 from deeprobust.graph.defense.gcn_preprocess import GCNJaccard as Jaccard
 @DataPreprocessorUniversalRegistry.register_data_preprocessor("gcnjaccard")
 class GCNJaccard(StructureEngineer):
+    """
+    GCNJaccard preprocesses input graph via droppining dissimilar
+    edges. See more details in
+    Adversarial Examples on Graph Data: Deep Insights into Attack and Defense,
+    https://arxiv.org/pdf/1903.01610.pdf.
+    """
     def __init__(self, threshold=0.01, *args, **kwargs):
+        """ drop dissimilar edges with similarity smaller than given threshold
+
+        Parameters
+        ----------
+        threshold : float
+            similarity threshold for dropping edges. If two connected nodes with similarity smaller than threshold, the edge between them will be removed.
+        """
         super(GCNJaccard, self).__init__(*args, **kwargs)
         self.engine=Jaccard(2,2,2)
         self.engine.threshold=threshold
     def _transform(self,data):
-        features=data.x
-        adj=data.edge_index
-        modified_adj = self.engine.drop_dissimilar_edges(features, adj)
-        data.edge_index=modified_adj
+        features = get_feature(data)
+        adj = get_edges(data) # edge list 
+        LOGGER.info(f'before modified: {adj.shape}')
+        adj = to_adjacency_matrix(adj).numpy() # adjacency matrix
+        modified_adj = self.engine.drop_dissimilar_edges(features, adj).toarray() # adjacency matrix
+        modified_adj = to_adjacency_list(torch.Tensor(modified_adj)) # edge list
+        LOGGER.info(f'after modified: {modified_adj.shape}' )
+        set_edges(data,modified_adj)
+        return data
+
+from deeprobust.graph.defense.gcn_preprocess import GCNSVD as SVD
+@DataPreprocessorUniversalRegistry.register_data_preprocessor("gcnsvd")
+class GCNSVD(StructureEngineer):
+    """GCNSVD uses Truncated SVD as preprocessing.See more details in All You Need Is Low (Rank): Defending
+    Against Adversarial Attacks on Graphs,
+    https://dl.acm.org/doi/abs/10.1145/3336191.3371789.
+    """
+    def __init__(self, k=50, threshold=0.05, *args, **kwargs):
+        """perform rank-k approximation of adjacency matrix via
+        truncated SVD
+
+        Parameters
+        ----------
+        k : int
+            number of singular values and vectors to compute.
+
+        threshold : float
+            edges with scores larger than threshold will be kept.
+        """
+        super(GCNSVD, self).__init__(*args, **kwargs)
+        self.engine=SVD(2,2,2)
+        self.k=k
+        self.threshold=threshold
+
+    def _transform(self,data):
+        adj = get_edges(data) # edge list
+        LOGGER.info(f'before modified: {adj.shape}')
+        adj = to_adjacency_matrix(adj).numpy() # adjacency matrix
+        modified_adj = self.engine.truncatedSVD(adj,self.k) # adjacency matrix
+        modified_adj = (modified_adj> self.threshold).astype(int)
+        modified_adj = to_adjacency_list(torch.Tensor(modified_adj)) # edge list
+        LOGGER.info(f'after modified: {modified_adj.shape}' )
+        set_edges(data,modified_adj)
         return data

test/preprocessing/fe.py

@@ -1,18 +0,0 @@
-from autogl.datasets import build_dataset_from_name
-data = build_dataset_from_name('cora')
-
-# 2. Compose a feature engineering pipeline
-from autogl.module.preprocessing.feature_engineering import EigenFeatureGenerator
-
-# you may compose feature engineering bases through autogl.module.feature._base_feature_engineer
-from autogl.module.preprocessing.structure_engineering._structure_engineer import *
-# fe = EigenFeatureGenerator(size=32)
-fe=GCNJaccard()
-
-# 3. Fit and transform the data
-data1=fe.fit_transform(data,inplace=False)
-
-# from autogl.data.graph import GeneralStaticGraph
-# print(isinstance(data, GeneralStaticGraph))
-# print(data.nodes)
-

test/preprocessing/structure.py

@@ -0,0 +1,11 @@
+from autogl.datasets import build_dataset_from_name
+data = build_dataset_from_name('cora')
+from autogl.module.preprocessing.structure_engineering._structure_engineer import *
+
+fes=[GCNJaccard,GCNSVD]
+for fe in fes:
+    print(f'Doing {fe}')
+    fe = fe()
+    data=fe.fit_transform(data,inplace=False)
+
+