A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

Related tags

Deep Learning TADDY_pytorch
Overview

TADDY: Anomaly detection in dynamic graphs via transformer

This repo covers an reference implementation for the paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

framework

Some codes are borrowed from Graph-Bert and NetWalk.

Requirments

  • Python==3.8
  • PyTorch==1.7.1
  • Transformers==3.5.1
  • Scipy==1.5.2
  • Numpy==1.19.2
  • Networkx==2.5
  • Scikit-learn==0.23.2

Usage

Step 0: Prepare Data

python 0_prepare_data.py --dataset uci

Step 1: Train Model

python 1_train.py --dataset uci --anomaly_per 0.1

Cite

If this code is helpful, please cite the original paper:

@ARTICLE{liu2021anomaly,
  author={Liu, Yixin and Pan, Shirui and Wang, Yu Guang and Xiong, Fei and Wang, Liang and Chen, Qingfeng and Lee, Vincent CS},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={Anomaly Detection in Dynamic Graphs via Transformer}, 
  year={2021},
  doi={10.1109/TKDE.2021.3124061}}

Don't forget to press a "star" after using!

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Comments
  • runtime error about the

    runtime error about the "data/eigen" folder

    Hi, I am running your code with the command: python 1_train.py --dataset uci --anomaly_per 0.1 after running: python 0_prepare_data.py --dataset uci And it shows that 'data/eigen/uci_0.5_0.1.pkl' not found, so I make a copy the same as which in the 'data/percent' folder. Is that correct? But actually I run into a bug with the traceback: $$$$ Start $$$$ Loading uci dataset... Loading eigen from: data/eigen/uci_0.5_0.1.pkl Traceback (most recent call last): File "1_train.py", line 56, in <module> setting_obj.run() File "/home/syj/syj_project/TADDY_pytorch/codes/Settings.py", line 8, in run loaded_data = self.dataset.load() File "/home/syj/syj_project/TADDY_pytorch/codes/DynamicDatasetLoader.py", line 173, in load adjs, eigen_adjs = self.get_adjs(rows, cols, weights, nb_nodes) File "/home/syj/syj_project/TADDY_pytorch/codes/DynamicDatasetLoader.py", line 134, in get_adjs eigen_adjs.append(np.array(eigen_adj_sparse.todense())) AttributeError: 'list' object has no attribute 'todense' And I totally have no idea why eigen_adj_sparse is a list? Could you please help me wtih this? Thanks in advance.

    PS: actually my running environment matches yours.

    opened by Switchsyj 1
  • ValueError: row index exceeds matrix dimensions

    ValueError: row index exceeds matrix dimensions

    File "C:\Users\16645\Desktop\代码\Anomaly Detection in Dynamic Graphs via\1_train.py", line 56, in setting_obj.run() File "C:\Users\16645\Desktop\代码\Anomaly Detection in Dynamic Graphs via\codes\Settings.py", line 8, in run loaded_data = self.dataset.load() File "C:\Users\16645\Desktop\代码\Anomaly Detection in Dynamic Graphs via\codes\DynamicDatasetLoader.py", line 173, in load adjs, eigen_adjs = self.get_adjs(rows, cols, weights, nb_nodes) File "C:\Users\16645\Desktop\代码\Anomaly Detection in Dynamic Graphs via\codes\DynamicDatasetLoader.py", line 142, in get_adjs adj = sp.csr_matrix((weights[i], (rows[i], cols[i])), shape=(nb_nodes, nb_nodes), dtype=np.float32) File "C:\aconda\envs\python38\lib\site-packages\scipy\sparse\compressed.py", line 54, in init other = self.class(coo_matrix(arg1, shape=shape)) File "C:\aconda\envs\python38\lib\site-packages\scipy\sparse\coo.py", line 196, in init self._check() File "C:\aconda\envs\python38\lib\site-packages\scipy\sparse\coo.py", line 283, in _check raise ValueError('row index exceeds matrix dimensions')

    opened by 1664587146 1
  • The “eigen” dataset is missing

    The “eigen” dataset is missing

    `def get_adjs(self, rows, cols, weights, nb_nodes):

        eigen_file_name = 'data/eigen/' + self.dataset_name + '_' + str(self.train_per) + '_' + str(self.anomaly_per) + '.pkl'
    if not os.path.exists(eigen_file_name):`

    Hello author, the above data set is missing in your code.

    opened by dawn9808 0
  • Incorporating Node/Edge features

    Incorporating Node/Edge features

    Hello,

    based on your code and paper, only topological time dependent Embeddings are generated. In the case of Network Intrusion Detection, for example, edge features are important to integrate in the embeddings. Is there a way to extend your model, so that it can also handel node/edge features?

    opened by jonnyfoka 0
Owner
Yue Tan
PhD student at UTS.
Yue Tan
GitHub
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