| 摘要: |
| 社交网络链路预测有助于揭示网络节点之间的潜在联系, 在好友推荐、合作预测等方面有着重要的实际应用价值. 然而, 现有的链路预测方法忽略了社交网络时间序列的中、长期发展趋势, 且没有从长期的角度考虑网络中节点之间的相互影响关系. 针对以上问题, 提出基于时空注意力的多粒度链路预测算法, 该算法能够融合不同粒度社交网络时间序列的时空特征以提升链路预测的准确性. 首先, 以时间衰减函数构建社交网络快照图的权重, 提出图加权移动平均策略, 生成反映短期、中期和长期趋势的不同粒度社交网络时间序列; 然后, 利用基于多头注意力机制的神经网络提取社交网络序列的全局时间特征; 接着, 结合社交网络序列内节点的历史交互信息, 通过基于掩码注意力机制的神经网络从长期角度自适应地构建网络拓扑结构, 以动态地调整节点之间的相互影响, 并结合图卷积网络建模空间信息; 最后, 提出融合注意力神经网络, 从短期、中期和长期时空特征中提取出有用的短期、中期和长期信息, 并进行特征融合, 准确地预测未来社交网络的链接. 在4种社交网络公开数据集上与7种现有的链路预测算法的实验对比证实所提方法的有效性和优越性. |
| 关键词: 社交网络 链路预测 多粒度 注意力机制 图卷积网络 |
| DOI：10.13328/j.cnki.jos.007286 |
| 分类号:TP311 |
| 基金项目:广东省自然科学基金面上项目(2023A1515011667); 深圳市科技重大专项(202302D074); 深圳市基础研究项目(JCYJ20220818100205012, JCYJ20210324093609026) |
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| Multi-granularity Link Prediction Algorithm Based on Spatiotemporal Attention |
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HE Yu-Lin1,2, LAI Jun-Long2, CUI Lai-Zhong2, YIN Jian-Fei2, HUANG Zhe-Xue1,2
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1.Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen), Shenzhen 518107, China;2.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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| Abstract: |
| Social network link prediction can help to reveal the potential connections between network nodes, and has important practical application value in friend recommendation and cooperation prediction. However, existing link prediction algorithms ignore the medium and long-term development trend of social network time series, and do not consider the interaction relationship between nodes in the network from a long-term perspective. To address the above-mentioned problems, a spatiotemporal attention-based multi-granularity link prediction algorithm is proposed, which can integrate the spatiotemporal features of social network time series with different granularities to improve the accuracy of link prediction. Firstly, the weights of the social network snapshot graph are constructed with the time decay function, and a graph-weighted moving average strategy is proposed to generate social network time series with different granularities reflecting short-term, medium-term, and long-term trends. Then, a neural network based on the multi-head attention mechanism is used to extract the global temporal features of social network sequences. Next, the historical interaction information of nodes within social network sequences is combined, and the neural network based on the mask attention mechanism is used to adaptively construct the network topology from a long-term perspective to dynamically adjust the interactions between nodes and is combined with graph convolutional network to model spatial information. Finally, the fusion attention neural network is proposed to extract useful short-term, medium-term and long-term information from short-term, medium-term and long-term spatiotemporal features, and perform feature fusion to accurately predict the future links of social networks. Experimental comparisons with seven existing link prediction algorithms on four social network public datasets confirm the effectiveness and superiority of the proposed method. |
| Key words: social network link prediction multi-granularity attention mechanism graph convolutional network (GCN) |
