Influence-aware Causal Autoencoder Network for Node Importance Ranking in Complex Networks

• URL: http://arxiv.org/abs/2511.01228v1
• Date: Mon, 03 Nov 2025 05:01:22 GMT
• Title: Influence-aware Causal Autoencoder Network for Node Importance Ranking in Complex Networks
• Authors: Jiahui Gao, Kuang Zhou, Yuchen Zhu,
• Abstract summary: We propose the Influence-aware Causal Autoencoder Network (ICAN), a novel framework that leverages causal representation learning to get robust, invariant node embeddings for cross-network ranking tasks.<n>ICAN consistently outperforms state-of-the-art baselines in terms of both ranking accuracy and generalization capability.
• Score: 18.40618637794483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Node importance ranking is a fundamental problem in graph data analysis. Existing approaches typically rely on node features derived from either traditional centrality measures or advanced graph representation learning methods, which depend directly on the target network's topology. However, this reliance on structural information raises privacy concerns and often leads to poor generalization across different networks. In this work, we address a key question: Can we design a node importance ranking model trained exclusively on synthetic networks that is effectively appliable to real-world networks, eliminating the need to rely on the topology of target networks and improving both practicality and generalizability? We answer this question affirmatively by proposing the Influence-aware Causal Autoencoder Network (ICAN), a novel framework that leverages causal representation learning to get robust, invariant node embeddings for cross-network ranking tasks. Firstly, ICAN introduces an influence-aware causal representation learning module within an autoencoder architecture to extract node embeddings that are causally related to node importance. Moreover, we introduce a causal ranking loss and design a unified optimization framework that jointly optimizes the reconstruction and ranking objectives, enabling mutual reinforcement between node representation learning and ranking optimization. This design allows ICAN, trained on synthetic networks, to generalize effectively across diverse real-world graphs. Extensive experiments on multiple benchmark datasets demonstrate that ICAN consistently outperforms state-of-the-art baselines in terms of both ranking accuracy and generalization capability.

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