Graph-structured Data-driven Topology Inference for Non-cooperative Clustered Wireless Communication Networks

HOU Changbo; FU Dingyi; SONG Zhen; WANG Bin; ZHOU Zhichao

doi:10.11999/JEIT250084

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HOU Changbo, FU Dingyi, SONG Zhen, WANG Bin, ZHOU Zhichao. Graph-structured Data-driven Topology Inference for Non-cooperative Clustered Wireless Communication Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250084

Citation:

HOU Changbo, FU Dingyi, SONG Zhen, WANG Bin, ZHOU Zhichao. Graph-structured Data-driven Topology Inference for Non-cooperative Clustered Wireless Communication Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250084

Citation:

HOU Changbo, FU Dingyi, SONG Zhen, WANG Bin, ZHOU Zhichao. Graph-structured Data-driven Topology Inference for Non-cooperative Clustered Wireless Communication Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250084

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Graph-structured Data-driven Topology Inference for Non-cooperative Clustered Wireless Communication Networks

doi: 10.11999/JEIT250084 cstr: 32379.14.JEIT250084

HOU Changbo^{1, 2},
FU Dingyi^{1
,
,},
SONG Zhen¹,
WANG Bin¹,
ZHOU Zhichao¹

1.
College of Information And Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (U23A20271), The Fundamental Research Funds for the Central Universities (3072024XX0808)

Received Date: 2025-02-12
Rev Recd Date: 2025-09-15

Available Online: 2025-09-20

Abstract

Abstract

Objective The emergence of clustered target communication networks complicates electromagnetic environment detection in non-cooperative scenarios, creating challenges for electromagnetic situation awareness and electronic countermeasures. Existing research seldom addresses topology prediction under conditions with no prior knowledge, where the absence of explicit structural information and the dynamic nature of the networks hinder accurate inference. This study investigates topology prediction for non-cooperative clustered wireless communication networks using graph-structured data-driven approaches. Specifically, it evaluates the performance of multiple topology inference methods, including the Multivariate Hawkes Process (MHP), Peter–Clark Momentary Conditional Independence (PCMCI), Graph Encoder–Decoder (GED), and Graph Convolutional Networks (GCN). The effects of network properties such as node count and edge probability on inference accuracy are analyzed. Additionally, a hybrid framework that integrates statistical models with graph-based learning is explored to improve inference accuracy and computational efficiency. Methods The proposed methodology combines causal inference with Graph Neural Network (GNN)–based learning. Adjacency matrices are first generated through causal discovery, using time-domain matrices derived from simulated wireless communication events. These matrices are constructed by thresholding power spectra to yield binary communication states. The GNN module subsequently refines the causal discovery output by suppressing false positives and optimizing global topology through encoder–decoder operations with multi-head attention mechanisms. To assess robustness, synthetic datasets are generated with NS-3 simulations under varying conditions: edge probabilities (0.15–0.60), node densities (8–13 nodes), sampling durations (0.05–0.30 ms), and node feature completeness (partial, 50%; full, 100%). Connectivity patterns are modeled by incorporating distance-adjusted edge probabilities. Performance evaluation uses F1-score, accuracy, recall, and inference time, with systematic comparison across baseline models (MHP, PCMCI, GCN, GED) and hybrid variants (PCMCI+GED, MHP+GED). Results and Discussions The PCMCI+GED hybrid framework consistently achieves superior topology prediction across diverse network configurations. At an edge probability of 0.45, PCMCI+GED with full node features attains an F1-score of 0.808, exceeding the performance of standalone PCMCI and GED by 31.1% and 4.9%, respectively (Fig. 7). This improvement arises from the synergy between causal priors and graph neural networks: PCMCI establishes preliminary causal relationships, while GED refines inference through global attention mechanisms that reduce false positives. Comparative analysis reveals that richer node features enhance topology inference in causal inference methods (Fig. 7). For example, MHP+GED with full features exceeds its 50% feature counterpart by 2.10%, and PCMCI+GED with full features improves by 3.04%. Yet, the most substantial gains come from combining causal inference with GED. Relative to standalone MHP with full features, MHP+GED improves by 30.65% with 50% features and 33.40% with full features. Similarly, PCMCI+GED improves by 34.43% and 38.51% under the same conditions. In contrast, relying solely on GNNs proves insufficient for modeling causal relationships. GED alone performs similarly to GCN, with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values of 0.0149 and 0.0206, respectively (Fig. 8). Without causal priors, GED offers no significant advantage over GCN; however, when priors are incorporated, GED outperforms GCN in inference accuracy (Fig. 9). Sampling duration analysis shows that 0.1 ms is optimal, balancing redundancy and information loss (Fig. 10, Table 2). Shorter intervals (0.05 ms) inflate computational costs through high-dimensional operations, whereas longer intervals (0.30 ms) obscure temporal dependencies, reducing the F1-score of PCMCI+GED with full features to 40.57% of its value at 0.1 ms. Efficiency evaluations highlight trade-offs between accuracy and runtime. With 50% node features, PCMCI+GED reduces inference time by 88.63% while retaining 96.96% of its F1-score. Under the same conditions, MHP+GED reduces inference time by 85.48% with only a 2.07% drop in performance (Fig. 11). PCMCI’s exponential complexity makes it computationally prohibitive in high-dimensional settings, whereas MHP’s quadratic scaling with node count and linear scaling with event frequency yield more modest efficiency gains. In low-dimensional settings, however, MHP’s event-driven computation leads to longer runtimes than PCMCI. Heatmap analysis further confirms the precision of the hybrid models. Adjacency matrices generated by PCMCI+GED and MHP+GED with full features closely align with the ground truth, demonstrating high predictive accuracy (Fig. 9). In sparse networks, standalone PCMCI introduces noise by linking non-interacting nodes, while GCN generates fragmented predictions due to the absence of causal priors. The hybrid framework alleviates these limitations by combining PCMCI’s local causal inference with GED’s global optimization. Overall, the hybrid framework addresses key shortcomings of individual methods: the high computational cost of PCMCI and MHP, and the limited interpretability of GNNs. By integrating causal discovery with graph-based deep learning, the model achieves state-of-the-art predictive accuracy while maintaining scalability. Its performance highlights the potential for real-time applications in resource-constrained environments, emphasizing the importance of balancing causal priors and data-driven learning for advancing non-cooperative wireless communication network analysis. Conclusions This study demonstrates the effectiveness of combining PCMCI-based causal inference with GED-enhanced GNN for topology prediction in non-cooperative clustered wireless communication networks. The hybrid model achieves state-of-the-art accuracy, particularly in dense networks, while partial node feature inputs substantially reduce computational overhead. Although the exponential complexity of PCMCI constrains scalability in high-dimensional settings, integration with GED alleviates this limitation through feature reduction and global optimization. The findings highlight the need to balance accuracy and efficiency in practical applications, where GCN offers a viable option for real-time inference. Future research will explore attention mechanisms and self-supervised learning to further enhance robustness. These advancements hold promise for improving electromagnetic situation awareness and electronic countermeasure strategies in dynamic adversarial environments.
- Clustered wireless communication networks,
- Network topology inference,
- Causal inference,
- Graph Neural Networks (GNN)

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References(32)

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