MANE: Organizational Network Embedding With Multiplex Attentive Neural Networks
MANE: Organizational Network Embedding With Multiplex Attentive Neural Networks
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MANE：基于多重注意力神经网络的组织网络嵌入
DOI：
10.1109/tkde.2022.3140866
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发表时间：
2023-04
影响因子：
8.9
通讯作者：
Yuyang Ye;Zheng Dong;Hengshu Zhu;Tong Xu;Xin Song;Runlong Yu;Hui Xiong
中科院分区：
文献类型：
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作者：
Yuyang Ye;Zheng Dong;Hengshu Zhu;Tong Xu;Xin Song;Runlong Yu;Hui Xiong
每个组织都有交流思想和信息的组织网络。人们相信，组织网络分析(ONA)可以帮助业务更有效地开展。虽然人们在组织网络中的关系可视化和分析方面做了大量的研究工作，但缺乏一种整体的方法来建模这些网络中复杂的社会结构和丰富的语义信息。事实上，员工的行为可能会发生在不同的通信平台上，如电子邮件和即时消息系统，这自然会导致组织社交网络的多元化结构。同时，对员工属性、组织结构图等语义信息的影响以及员工之间的协作关系进行建模也是一个挑战。为此，本文提出了一种对组织社会网络进行整体建模的多重注意力网络嵌入(MANE)方法。具体地说，我们首先提出了一种多属性随机游走方法来联合建模多个网络，并集成了外部工作信息。然后，我们通过最大化基于周围上下文节点预测中心节点的概率来保持网络结构。特别是，在训练过程中，我们引入了一种注意力机制，根据上下文节点与中心节点的属性关系和结构关系，利用k-core算法和最短路径算法为每个上下文节点赋权。这样，嵌入结果可以与它们的结构关系保持一致。此外，为了解决一些部门级任务，我们引入了一种细心的关系转换方法来学习组织网络中部门的表示。最后，我们利用真实数据对MANE在员工绩效预测、员工离职预测和部门绩效预测三个重要的人才管理任务上的性能进行了评估。我们还进行了链接预测任务，以验证员工嵌入的有效性。实验结果表明了MANE用于组织网络分析的有效性和可解释性。
Every organization has organizational networks for exchange of ideas and information. It is believed that organizational network analysis (ONA) can help the business be more effective. While considerable research efforts have been made for visualizing and analyzing relationships in organizational networks, it lacks a holistic way to model the complex social structures and rich semantic information of these networks. Indeed, employee behaviors can occur across different communication platforms, such as email and instant messaging systems, which naturally lead to the multiplex structure of organizational social networks. Meanwhile, it is also a challenge to model the impact of semantic information, such as employee attributes and organization charts, and the collaboration relationships of employees. To this end, in this paper, we propose a Multiplex Attentive Network Embedding (MANE) approach for modeling organizational social networks in a holistic way. Specifically, we first develop a multiple attributed random walk approach to jointly model multiple networks, with the integration of external work information. Then, we preserve the network structure by maximizing the probability of predicting the central node based on the surrounding context nodes. In particular, we introduce an attention mechanism to assign a weight to each context node in the training process, according to its attributed relation and structural relation with the central node by utilizing the k-core algorithm and the shortest path algorithm. In this way, the embedding results can be kept consistent with their structural relationships. Furthermore, to solve some department-level tasks, we introduce an attentive relational transition method to learn the representation of departments in the organizational networks. Finally, we evaluate the performance of MANE with extensive experiments on real-world data for three important talent management tasks, namely employee performance prediction, employee turnover prediction and department performance prediction. We also conduct a link prediction task to validate the effectiveness of employee embedding. Experimental results clearly show the effectiveness and interpretability of MANE for organizational network analysis.