近日，实验室王治政博士关于图表示学习的研究成果被人工智能领域顶级期刊IEEE Transactions on Neural Networks and learning systems (TNNLS)录用，TNNLS为CCF推荐期刊B类，中科院一区期刊，影响因子10.4。

  Title: Temporal Network Embedding Enhanced With Long-Range Dynamics and Self-Supervised Learning

  Abstract: Temporal network embedding (TNE) has promoted the research of knowledge discovery and reasoning on networks. It aims to embed vertices of temporal networks into a low-dimensional vector space while preserving network structures and temporal properties. However, most existing methods have limitations in capturing dynamics over long distances, which makes it difficult to explore multihop topological associations among vertices. To tackle this challenge, we propose LongTNE, which learns the long-range dynamics of vertices to endow TNE with the ability to capture high-order proximity (HP) of networks. In LongTNE, we employ graph self-supervised learning (Graph SSL) to optimize the establishment probability of deep links in each network snapshot. We also present an accumulated forward update (AFU) module to fathom global temporal evolution among multiple network snapshots. The empirical results on six temporal networks demonstrate that, in addition to achieving state-of-the-art performance on network mining tasks, LongTNE can be handily extended to existing TNE methods.

  题目：通过长程动态和自监督学习增强时序网络嵌入

  摘要：时序网络嵌入 (TNE) 推动了网络知识发现和推理的研究。它旨在将时序网络的顶点嵌入到低维向量空间中，同时保留网络结构和时间属性。然而，大多数现有方法在捕捉长距离动态方面存在局限性，这使得探索顶点之间的多跳拓扑关联变得困难。为了应对这一挑战，我们提出了 LongTNE，它学习顶点的长距离动态，使 TNE 能够捕捉网络的高阶邻近度 (HP)。在 LongTNE 中，我们采用图自监督学习 (Graph SSL) 来优化每个网络快照中深度链接的建立概率。我们还提出了一个累积前向更新 (AFU) 模块来揭示多个网络快照之间的全局时间演变。六个时间网络的实证结果表明，除了在网络挖掘任务上实现最先进的性能外，LongTNE 还可以轻松扩展到现有的 TNE 方法。