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一种基于博弈论的时序网络链路预测方法

刘留, 王煜尧, 倪琦瑄, 曹杰, 卜湛

刘留, 王煜尧, 倪琦瑄, 曹杰, 卜湛. 一种基于博弈论的时序网络链路预测方法[J]. 计算机研究与发展, 2019, 56(9): 1953-1964. DOI: 10.7544/issn1000-1239.2019.20180842
引用本文: 刘留, 王煜尧, 倪琦瑄, 曹杰, 卜湛. 一种基于博弈论的时序网络链路预测方法[J]. 计算机研究与发展, 2019, 56(9): 1953-1964. DOI: 10.7544/issn1000-1239.2019.20180842
Liu Liu, Wang Yuyao, Ni Qixuan, Cao Jie, Bu Zhan. A Link Prediction Approach in Temporal Networks Based on Game Theory[J]. Journal of Computer Research and Development, 2019, 56(9): 1953-1964. DOI: 10.7544/issn1000-1239.2019.20180842
Citation: Liu Liu, Wang Yuyao, Ni Qixuan, Cao Jie, Bu Zhan. A Link Prediction Approach in Temporal Networks Based on Game Theory[J]. Journal of Computer Research and Development, 2019, 56(9): 1953-1964. DOI: 10.7544/issn1000-1239.2019.20180842

一种基于博弈论的时序网络链路预测方法

基金项目: 国家自然科学基金项目(71871109,91646204,71801123,71871233)
详细信息
  • 中图分类号: TP393

A Link Prediction Approach in Temporal Networks Based on Game Theory

Funds: This work was supported by the National Natural Science Foundation of China (71871109, 91646204, 71801123, 71871233).
  • 摘要: 链路预测是复杂网络分析领域的一项重要研究课题,可被应用于许多实际应用场景,如推荐系统、信息检索和市场分析等.不同于传统的链路预测问题,针对有时间窗口的时序链路集合,需预测未来任意时刻链路的存在情况,即探究时序网络的演化机制.为解决这一问题,结合生存分析和博弈论,提出一种有效的半监督学习框架.首先,定义一个ε-邻接网络序列模型,并利用每条链路的时间戳信息生成真实的网络演化序列.为捕捉网络演化规律,为每条链路定义一组基于邻居相似性的特征向量,并采用Cox比例风险模型来估计该特征向量的协变量系数.为缩小搜索空间,提出一种基于博弈的双向选择机制来预测未来的网络拓扑结构.最后,提出一种基于多智能体自治计算的网络演化预测算法,并在多个真实时序网络数据集上验证了算法的有效性和高效性.
    Abstract: Link prediction is an important task in complex network analysis, which can be applied to many real-world practical scenarios such as recommender systems, information retrieval, and marketing analysis. Different from the traditional link prediction problem, this paper predicts the existence of the link at any time in the future based on the set of temporal links in a given time window, that is, the evolution mechanism of the temporal network. To explore this question, we propose a novel semi-supervised learning framework, which integrates both survival analysis and game theory. First, we carefully define the ε-adjacent network sequence, and make use of time stamp on each link to generate the ground-truth network evolution sequence. Next, to capture the law of network evolution, we employ the Cox proportional hazard model to study the relative hazard associated with each temporal link, so as to estimate the covariate’s coefficient associated with a set of neighborhood-based proximity features. To compress the searching space, we further propose a game theory based two-way selection mechanism to inference the future network topology. We finally propose a network evolution prediction algorithm based on autonomy-oriented computing, and demonstrate both the effectiveness and the efficiency of the proposed algorithm on real-world temporal networks.
  • 期刊类型引用(6)

    1. 邬剑升,李玉珩. 基于共同邻居惩罚的复杂网络链路预测方法. 计算机测量与控制. 2023(03): 71-75+139 . 百度学术
    2. 王子健,薛家玥,杨鹏飞,李艺茹,相洁. 基于对抗生成网络的时序脑功能网络预测方法. 太原理工大学学报. 2023(05): 830-837 . 百度学术
    3. 康驻关,金福生,王国仁. 基于Motif聚集系数与时序划分的高阶链接预测方法. 软件学报. 2021(03): 712-725 . 百度学术
    4. 高雅娟,王玉峰. 融合多维特征的ISP网络拓扑匹配优化仿真. 计算机仿真. 2021(02): 278-281+286 . 百度学术
    5. 顾秋阳,吴宝,池仁勇. 基于高阶路径相似度的复杂网络链路预测方法. 通信学报. 2021(07): 61-69 . 百度学术
    6. 王瑾. 动态有向网络中的时序链路预测问题研究. 粘接. 2021(09): 106-109 . 百度学术

    其他类型引用(8)

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出版历程
  • 发布日期:  2019-08-31

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