Official implementation of our CIKM 2025 work "Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data"

run code

run command:

python main_newdiff.py --dataset [dataset]

Available datasets:(as in datasets\synthesis)

• android_10_30, christianity_10_30, jazz_IC50, jazz_LT50, jazz_SIS50, netscience_IC50, netscience_LT50, netscience_SIS50, power_IC50, power_LT50, power_SIS50,

• Some large datasets files are not included in the repository. Find them in the following link: https://drive.google.com/drive/folders/1Ga7qTNscLug49o69E18AAyiXvRKDjjY5?usp=sharing

Tunable params can be checked in the following description.

load saved model to run code

run command:

python main_newdiff.py --dataset [dataset] --save_dict [path to saved model]

e.g.

python main_newdiff.py --dataset jazz_IC50 --state_dict ./saved_diffusers/jazz_IC50_best.pt

Requirements

Please check requirements.txt for the required packages.

Main Parameters

• dataset: Name of the dataset to use
• gnn_type: Type of GNN architecture (default: gcn)
• noise_emb_dim: Dimension of noise embedding (default: 128)
• hidden_dim: Hidden layer dimension (default: 128)
• num_layers: Number of GNN layers (default: 5)
• activation: Activation function (default: prelu)
• mlp_layers: Number of MLP layers (default: 4)
• num_advisors: Number of advisor models (default: 1)

Optional Parameters

• feat_drop: Feature dropout rate (default: 0.0)
• attn_drop: Attention dropout rate (default: 0.0)
• negative_slope: Negative slope for LeakyReLU (default: 0.2)
• residual: Whether to use residual connections (default: True)
• scheduler: Whether to use learning rate scheduler (default: True)
• train_cond: Whether to train the condition module (default: False)

Cite this paper

@article{chen2025structure,
  title={Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data},
  author={Chen, Hongyi and Ding, Jingtao and Liang, Xiaojun and Li, Yong and Zhang, Xiao-Ping},
  journal={arXiv e-prints},
  pages={arXiv--2502},
  year={2025}
}