Deep Contrastive Multiview Network Embedding | |
Mengqi Zhang1,2![]() ![]() ![]() ![]() | |
2022-10-17 | |
会议名称 | ACM International Conference on Information and Knowledge Management |
会议录名称 | Proceedings of the 31st ACM International Conference on Information and Knowledge Management |
会议日期 | 2022-10-17 |
会议地点 | New York, NY, USA |
摘要 | Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive learning approaches have shown promising performance in this task. However, they neglect the semantic consistency between fused and view representations and have difficulty in modeling complementary information between different views. To deal with these deficiencies, this work presents a novel Contrastive leaRning framEwork for Multiview network Embedding (CREME). In our work, different views can be obtained based on the various relations among nodes. Then, we generate view embeddings via proper view encoders and utilize an attentive multiview aggregator to fuse these representations. Particularly, we design two collaborative contrastive objectives, view fusion InfoMax and inter-view InfoMin, to train the model in a self-supervised manner. The former objective distills information from embeddings generated from different views, while the latter captures complementary information among views to promote distinctive view embeddings. We also show that the two objectives can be unified into one objective for model training. Extensive experiments on three real-world datasets demonstrate that our proposed CREME is able to consistently outperform state-of-the-art methods. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 数据挖掘 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52307 |
专题 | 模式识别实验室 |
通讯作者 | Shu Wu |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Mengqi Zhang,Yanqiao Zhu,Qiang Liu,et al. Deep Contrastive Multiview Network Embedding[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
论文5-Deep Contrastive(1307KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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