Abstract:

Academic community identification in citation networks aims at uncovering internal correlations among scholars. However， heterogeneity of node attributes （content features） and topological structures （association patterns） often cause feature misalignment and information dilution during fusion， and the existing methods often ignore stable association patterns in core subnetworks formed by highly-connected nodes， which limit identification accuracy. To address these issues， a Multi-view Attention Fusion for academic Community Identification （MAFCI） method was proposed. In the method， a dual-view design was adopted： original node attributes and the adjacency matrix were encoded by one view， while core-node information and its topology extracted by k?core were incorporated by the other one view. A sparse graph attention mechanism was applied to extract low-dimensional embeddings from both views， and a dynamic weighting strategy was employed to fuse multi-source information adaptively， thereby balancing local structure and global semantics. In the decoder， the adjacency matrix was reconstructed via inner product and node attributes as well as core information were restored using attention mechanism. At the same time， a self-supervised clustering loss was introduced to guide embedding representations in converging to discriminative cluster centers and enhance community boundary separability. Experimental results on four citation network datasets （Cora， Citeseer， ACM， and DBLP） demonstrate that MAFCI method outperforms nine advanced baseline methods， including DDGAE （Deep Dual Graph Attention auto-Encoder） and BCDAN （Balanced method for Community Detection in Attribute Networks）， on most core metrics. Specifically， on the DBLP dataset， MAFCI method achieves a Normalized Mutual Information （NMI） of 0.513 and an Accuracy （ACC） of 0.809， which are improved by 2.3 and 1.3 percentage points， respectively， compared to the optimal baseline method； on the ACM dataset， MAFCI method achieves an NMI of 0.674 and an F1-score of 0.903， representing improvements of 1.2 and 0.8 percentage points， respectively， compared to the optimal baseline method； on the Citeseer dataset， MAFCI method has the NMI achieved 0.448， which is improved by 0.7 percentage points； on the Cora dataset， MAFCI method has the NMI reached 0.543， also surpassing all comparison methods. It can be seen that MAFCI method enhances the accuracy and robustness of academic community identification and has strong generalization across citation networks of varying scales and structures effectively through dual-view sparse attention and dynamic fusion mechanism.

Key words: academic community identification, community detection, self-supervised clustering, core subnetwork, sparse graph attention, joint optimization, representation learning

摘要：

引文网络中的学术群体识别旨在揭示学者间的内在关联。然而，节点属性（内容特征）与拓扑结构（关联模式）的异质性导致融合时易出现特征错位与信息稀释，且现有方法常忽略由高连接度节点构成的核心子网络所蕴含的稳定关联模式，这些制约了识别精度。为了解决上述问题，提出一种多视图注意力融合的学术群体识别（MAFCI）方法。所提方法采用双视图设计：一个视图对原始节点属性与邻接矩阵进行编码，另一个视图融合由k-core提取的核心节点信息及其拓扑结构；采用稀疏图注意力机制分别从两个视图中提取低维嵌入，并通过动态加权策略实现多源信息的自适应融合，以兼顾局部结构与全局语义。解码器通过内积重构邻接矩阵，并基于注意力机制恢复节点属性与核心信息；同时，引入自监督聚类损失，引导嵌入表示向判别性聚类中心收敛，增强群体边界的区分度。在Cora、Citeseer、ACM和DBLP 4个引文网络数据集上的实验结果表明，MAFCI方法在多数核心评价指标上优于DDGAE（Deep Dual Graph Attention auto-Encoder）和BCDAN（Balanced method for Community Detection in Attribute Networks）等9个先进基线方法。具体地，在DBLP数据集上，MAFCI方法的归一化互信息（NMI）达到0.513，准确率（ACC）达到0.809，分别较最优基线方法提升了2.3和1.3个百分点；在ACM数据集上，MAFCI方法的NMI为0.674，F1分数为0.903，分别较最优基线方法提升了1.2和0.8个百分点；在Citeseer数据集上，MAFCI方法的NMI达到0.448，较最优基线方法提升了0.7个百分点；在Cora数据集上，MAFCI方法的NMI达到0.543，同样优于所有对比方法。可见，MAFCI方法通过双视图稀疏注意力与动态融合机制，有效提升了学术群体识别的准确性与鲁棒性，在不同规模与结构的引文网络上均表现出良好泛化能力。

关键词: 学术群体识别, 社区检测, 自监督聚类, 核心子网络, 稀疏图注意力, 联合优化, 表示学习