时间一致网络的链接预测
Link Prediction for Temporally Consistent Networks
摘要：
动态网络具有内在的结构、计算和多学科优势。链接预测估计动态网络中的下一个关系。然而，在当前的链接预测方法中，仅考虑二分或非二分但同质的网络。使用邻接矩阵来表示动态演化的网络限制了从异构、稀疏或形成的网络中分析学习的能力。在异构网络的情况下，使用二进制值矩阵对所有网络状态进行建模可能是困难的。另一方面，稀疏或当前形成的网络具有许多缺失的边缘，这些边缘被表示为零，从而引入类不平衡或噪声。我们提出了一个时间参数化矩阵（TP矩阵），并实证证明其在非二分异构网络的有效性。此外，我们提出了一个预测影响力指数作为衡量一个节点的提升或减少预测的影响，使用向后和向前看的最大化的时间空间的n度邻域。我们进一步提出了一种新的方法，规范表示异构的时间演化活动的时间参数化网络模型（TPNM）。新方法鲁棒地使活动被表示为网络的形式，从而潜在地激发新的链接预测应用，包括智能业务流程管理系统和上下文感知工作流引擎。我们在不同网络系统的四个数据集上评估了我们的模型。我们目前的结果表明，该模型是更有效地捕捉和保持动态演变的网络中的时间关系。我们还表明，我们的模型比最先进的链接预测基准测试结果的网络是敏感的时间演变。
英文摘要：
Dynamic networks have intrinsic structural, computational, and multidisciplinary advantages. Link prediction estimates the next relationship in dynamic networks. However, in the current link prediction approaches, only bipartite or non-bipartite but homogeneous networks are considered. The use of adjacency matrix to represent dynamically evolving networks limits the ability to analytically learn from heterogeneous, sparse, or forming networks. In the case of a heterogeneous network, modeling all network states using a binary-valued matrix can be difficult. On the other hand, sparse or currently forming networks have many missing edges, which are represented as zeros, thus introducing class imbalance or noise. We propose a time-parameterized matrix (TP-matrix) and empirically demonstrate its effectiveness in non-bipartite, heterogeneous networks. In addition, we propose a predictive influence index as a measure of a node's boosting or diminishing predictive influence using backward and forward-looking maximization over the temporal space of the n-degree neighborhood. We further propose a new method of canonically representing heterogeneous time-evolving activities as a temporally parameterized network model (TPNM). The new method robustly enables activities to be represented as a form of a network, thus potentially inspiring new link prediction applications, including intelligent business process management systems and context-aware workflow engines. We evaluated our model on four datasets of different network systems. We present results that show the proposed model is more effective in capturing and retaining temporal relationships in dynamically evolving networks. We also show that our model performed better than state-of-the-art link prediction benchmark results for networks that are sensitive to temporal evolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
