用新模型评估论文知识价值，兼顾效率与跨领域鲁棒性
EMK-KEN: A High-Performance Approach for Assessing Knowledge Value in Citation Network
- 分两模块：先用MetaFP+Mamba提取文本语义，再用KAN捕捉引用网络结构
- 在10个数据集上优于现有方法，尤其在跨领域场景表现更稳
- 适合需要精准评估论文影响力的科研人员或期刊审稿人
随着学术文献的爆炸式增长，高效评估文献的知识价值变得至关重要。然而，现有方法多聚焦于建模整个引用网络，其结构复杂且在处理文本嵌入时常面临长序列依赖问题，导致效率低下且在不同领域中鲁棒性差。为此，本文提出一种名为EMK-KEN的新方法。该模型包含两个模块：第一模块利用MetaFP和Mamba捕捉节点元数据与文本嵌入的语义特征，学习每篇论文的上下文表示；第二模块利用KAN进一步捕捉引用网络的结构信息，以区分不同领域的网络差异。基于十个基准数据集的大量实验表明，所提方法在有效性和鲁棒性上均优于当前最优竞争模型。
原文摘要 · Abstract (English)
With the explosive growth of academic literature, effectively evaluating the knowledge value of literature has become quite essential. However, most of the existing methods focus on modeling the entire citation network, which is structurally complex and often suffers from long sequence dependencies when dealing with text embeddings. Thus, they might have low efficiency and poor robustness in different fields. To address these issues, a novel knowledge evaluation method is proposed, called EMK-KEN. The model consists of two modules. Specifically, the first module utilizes MetaFP and Mamba to capture semantic features of node metadata and text embeddings to learn contextual representations of each paper. The second module utilizes KAN to further capture the structural information of citation networks in order to learn the differences in different fields of networks. Extensive experiments based on ten benchmark datasets show that our method outperforms the state-of-the-art competitors in effectiveness and robustness.
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