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README.md

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-# GraphCTA
+# GraphCTA
+Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation (WWW 2024)
+
+![](https://github.com/cszhangzhen/GraphCTA/blob/main/fig/model.png)
+
+This is a PyTorch implementation of the GraphCTA algorithm, which tries to address the domain adaptation problem without accessing the labelled source graph. It performs model adaptation and graph adaptation collaboratively through a series of procedures: (1) conduct model adaptation based on node's neighborhood predictions in target graph considering both local and global information; (2) perform graph adaptation by updating graph structure and node attributes via neighborhood constrastive learning; and (3) the updated graph serves as an input to facilitate the subsequent iteration of model adaptation, thereby establishing a collaborative loop between model adaptation and graph adaptation.
+
+
+## Requirements
+* python3.8
+* pytorch==2.0.0
+* torch-scatter==2.1.1+pt20cu118
+* torch-sparse==0.6.17+pt20cu118
+* torch-cluster==1.6.1+pt20cu118
+* torch-geometric==2.3.1
+* numpy==1.24.3
+* scipy==1.10.1
+* tqdm==4.65.0
+
+## Datasets
+Datasets used in the paper are all publicly available datasets. You can find [Elliptic](https://www.kaggle.com/datasets/ellipticco/elliptic-data-set), [Twitch](https://github.com/benedekrozemberczki/datasets#twitch-social-networks) and [Citation](https://github.com/yuntaodu/ASN/tree/main/data) via the links.
+
+## Quick Start:
+Just execuate the following command for source model pre-training:
+```
+python train_source.py
+```
+Then, execuate the following command for adaptation:
+```
+python train_target.py
+```

datasets.py

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+import os.path as osp
+import torch
+import numpy as np
+from torch_geometric.data import InMemoryDataset, Data
+from torch_geometric.io import read_txt_array
+import torch.nn.functional as F
+
+import scipy
+import pickle as pkl
+import csv
+import json
+
+import warnings
+warnings.filterwarnings('ignore', category=DeprecationWarning)
+
+
+class CitationDataset(InMemoryDataset):
+    def __init__(self,
+                 root,
+                 name,
+                 transform=None,
+                 pre_transform=None,
+                 pre_filter=None):
+        self.name = name
+        self.root = root
+        super(CitationDataset, self).__init__(root, transform, pre_transform, pre_filter)
+
+        self.data, self.slices = torch.load(self.processed_paths[0])
+
+    @property
+    def raw_file_names(self):
+        return ["docs.txt", "edgelist.txt", "labels.txt"]
+
+    @property
+    def processed_file_names(self):
+        return ['data.pt']
+
+    def download(self):
+        pass
+
+    def process(self):
+        edge_path = osp.join(self.raw_dir, '{}_edgelist.txt'.format(self.name))
+        edge_index = read_txt_array(edge_path, sep=',', dtype=torch.long).t()
+
+        docs_path = osp.join(self.raw_dir, '{}_docs.txt'.format(self.name))
+        f = open(docs_path, 'rb')
+        content_list = []
+        for line in f.readlines():
+            line = str(line, encoding="utf-8")
+            content_list.append(line.split(","))
+        x = np.array(content_list, dtype=float)
+        x = torch.from_numpy(x).to(torch.float)
+
+        label_path = osp.join(self.raw_dir, '{}_labels.txt'.format(self.name))
+        f = open(label_path, 'rb')
+        content_list = []
+        for line in f.readlines():
+            line = str(line, encoding="utf-8")
+            line = line.replace("\r", "").replace("\n", "")
+            content_list.append(line)
+        y = np.array(content_list, dtype=int)
+        y = torch.from_numpy(y).to(torch.int64)
+
+        data_list = []
+        data = Data(edge_index=edge_index, x=x, y=y)
+
+        random_node_indices = np.random.permutation(y.shape[0])
+        training_size = int(len(random_node_indices) * 0.8)
+        val_size = int(len(random_node_indices) * 0.1)
+        train_node_indices = random_node_indices[:training_size]
+        val_node_indices = random_node_indices[training_size:training_size + val_size]
+        test_node_indices = random_node_indices[training_size + val_size:]
+
+        train_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        train_masks[train_node_indices] = 1
+        val_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        val_masks[val_node_indices] = 1
+        test_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        test_masks[test_node_indices] = 1
+
+        data.train_mask = train_masks
+        data.val_mask = val_masks
+        data.test_mask = test_masks
+
+        if self.pre_transform is not None:
+            data = self.pre_transform(data)
+
+        data_list.append(data)
+
+        data, slices = self.collate([data])
+
+        torch.save((data, slices), self.processed_paths[0])
+
+
+class EllipticDataset(InMemoryDataset):
+    def __init__(self,
+                 root,
+                 name,
+                 transform=None,
+                 pre_transform=None,
+                 pre_filter=None):
+        self.name = name
+        self.root = root
+        super(EllipticDataset, self).__init__(root, transform, pre_transform, pre_filter)
+
+        self.data, self.slices = torch.load(self.processed_paths[0])
+
+    @property
+    def raw_file_names(self):
+        return [".pkl"]
+
+    @property
+    def processed_file_names(self):
+        return ['data.pt']
+
+    def download(self):
+        pass
+
+    def process(self):
+        path = osp.join(self.raw_dir, '{}.pkl'.format(self.name))
+        result = pkl.load(open(path, 'rb'))
+        A, label, features = result
+        label = label + 1
+        edge_index = torch.tensor(np.array(A.nonzero()), dtype=torch.long)
+        features = np.array(features)
+        x = torch.from_numpy(features).to(torch.float)
+        y = torch.tensor(label).to(torch.int64)
+
+        data_list = []
+        data = Data(edge_index=edge_index, x=x, y=y)
+
+        random_node_indices = np.random.permutation(y.shape[0])
+        training_size = int(len(random_node_indices) * 0.8)
+        val_size = int(len(random_node_indices) * 0.1)
+        train_node_indices = random_node_indices[:training_size]
+        val_node_indices = random_node_indices[training_size:training_size + val_size]
+        test_node_indices = random_node_indices[training_size + val_size:]
+
+        train_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        train_masks[train_node_indices] = 1
+        val_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        val_masks[val_node_indices] = 1
+        test_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        test_masks[test_node_indices] = 1
+
+        data.train_mask = train_masks
+        data.val_mask = val_masks
+        data.test_mask = test_masks
+
+        if self.pre_transform is not None:
+            data = self.pre_transform(data)
+
+        data_list.append(data)
+
+        data, slices = self.collate([data])
+
+        torch.save((data, slices), self.processed_paths[0])
+
+
+class TwitchDataset(InMemoryDataset):
+    def __init__(self,
+                 root,
+                 name,
+                 transform=None,
+                 pre_transform=None,
+                 pre_filter=None):
+        self.name = name
+        self.root = root
+        super(TwitchDataset, self).__init__(root, transform, pre_transform, pre_filter)
+
+        self.data, self.slices = torch.load(self.processed_paths[0])
+
+    @property
+    def raw_file_names(self):
+        return ["edges.csv, features.json, target.csv"]
+
+    @property
+    def processed_file_names(self):
+        return ['data.pt']
+
+    def download(self):
+        pass
+
+    def load_twitch(self, lang):
+        assert lang in ('DE', 'EN', 'FR'), 'Invalid dataset'
+        filepath = self.raw_dir
+        label = []
+        node_ids = []
+        src = []
+        targ = []
+        uniq_ids = set()
+        with open(f"{filepath}/musae_{lang}_target.csv", 'r') as f:
+            reader = csv.reader(f)
+            next(reader)
+            for row in reader:
+                node_id = int(row[5])
+                # handle FR case of non-unique rows
+                if node_id not in uniq_ids:
+                    uniq_ids.add(node_id)
+                    label.append(int(row[2]=="True"))
+                    node_ids.append(int(row[5]))
+
+        node_ids = np.array(node_ids, dtype=np.int32)
+
+        with open(f"{filepath}/musae_{lang}_edges.csv", 'r') as f:
+            reader = csv.reader(f)
+            next(reader)
+            for row in reader:
+                src.append(int(row[0]))
+                targ.append(int(row[1]))
+
+        with open(f"{filepath}/musae_{lang}_features.json", 'r') as f:
+            j = json.load(f)
+
+        src = np.array(src)
+        targ = np.array(targ)
+        label = np.array(label)
+
+        inv_node_ids = {node_id:idx for (idx, node_id) in enumerate(node_ids)}
+        reorder_node_ids = np.zeros_like(node_ids)
+        for i in range(label.shape[0]):
+            reorder_node_ids[i] = inv_node_ids[i]
+
+        n = label.shape[0]
+        A = scipy.sparse.csr_matrix((np.ones(len(src)), (np.array(src), np.array(targ))), shape=(n,n))
+        features = np.zeros((n,3170))
+        for node, feats in j.items():
+            if int(node) >= n:
+                continue
+            features[int(node), np.array(feats, dtype=int)] = 1
+        new_label = label[reorder_node_ids]
+        label = new_label
+
+        return A, label, features
+
+    def process(self):
+        A, label, features = self.load_twitch(self.name)
+        edge_index = torch.tensor(np.array(A.nonzero()), dtype=torch.long)
+        features = np.array(features)
+        x = torch.from_numpy(features).to(torch.float)
+        y = torch.from_numpy(label).to(torch.int64)
+
+        data_list = []
+        data = Data(edge_index=edge_index, x=x, y=y)
+
+        random_node_indices = np.random.permutation(y.shape[0])
+        training_size = int(len(random_node_indices) * 0.8)
+        val_size = int(len(random_node_indices) * 0.1)
+        train_node_indices = random_node_indices[:training_size]
+        val_node_indices = random_node_indices[training_size:training_size + val_size]
+        test_node_indices = random_node_indices[training_size + val_size:]
+
+        train_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        train_masks[train_node_indices] = 1
+        val_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        val_masks[val_node_indices] = 1
+        test_masks = torch.zeros([y.shape[0]], dtype=torch.bool)
+        test_masks[test_node_indices] = 1
+
+        data.train_mask = train_masks
+        data.val_mask = val_masks
+        data.test_mask = test_masks
+
+        if self.pre_transform is not None:
+            data = self.pre_transform(data)
+
+        data_list.append(data)
+
+        data, slices = self.collate([data])
+
+        torch.save((data, slices), self.processed_paths[0])