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[2511.01228] Importance Ranking in Complex Networks via Influence-aware Causal Node Embedding
Summary
This paper presents a novel framework for ranking node importance in complex networks using influence-aware causal node embedding, enhancing cross-network generalization and accuracy.
Why It Matters
Understanding node importance is crucial for applications like social recommendation and influence maximization. This research addresses limitations in existing methods by integrating causal representation learning, which improves ranking performance and generalization across different network structures, especially in privacy-sensitive contexts.
Key Takeaways
- Introduces a causal representation learning framework for node importance ranking.
- Enhances cross-network generalization by optimizing node embeddings.
- Demonstrates superior performance over traditional ranking methods in various datasets.
- Addresses challenges in scenarios with inaccessible network structures.
- Offers insights for applications in network analysis and engineering.
Computer Science > Social and Information Networks arXiv:2511.01228 (cs) [Submitted on 3 Nov 2025 (v1), last revised 15 Feb 2026 (this version, v3)] Title:Importance Ranking in Complex Networks via Influence-aware Causal Node Embedding Authors:Jiahui Gao, Kuang Zhou, Yuchen Zhu, Keyu Wu View a PDF of the paper titled Importance Ranking in Complex Networks via Influence-aware Causal Node Embedding, by Jiahui Gao and 2 other authors View PDF HTML (experimental) Abstract:Understanding and quantifying node importance is a fundamental problem in network science and engineering, underpinning a wide range of applications such as influence maximization, social recommendation, and network dismantling. Prior research often relies on centrality measures or advanced graph embedding techniques using structural information, followed by downstream classification or regression tasks to identify critical nodes. However, these approaches typically decouple node representation learning from the ranking objective and depend heavily on the topological structure of target networks, leading to feature-task inconsistency and poor cross-network generalization. This paper proposes a novel framework that leverages causal representation learning to obtain robust and invariant node embeddings for cross-network ranking tasks. Specifically, we introduce an influence-aware causal node embedding module within an autoencoder architecture to extract node embeddings that are causally related to node importan...
