Trajectory-User Linking (TUL) is crucial for human mobility modeling by
linking different trajectories to users with the exploration of complex
mobility patterns. Existing works mainly rely on the recurrent neural framework
to encode the temporal dependencies in trajectories, have fall short in
capturing spatial-temporal global context for TUL prediction. To fill this gap,
this work presents a new hierarchical spatio-temporal attention neural network,
called AttnTUL, to jointly encode the local trajectory transitional patterns
and global spatial dependencies for TUL. Specifically, our first model
component is built over the graph neural architecture to preserve the local and
global context and enhance the representation paradigm of geographical regions
and user trajectories. Additionally, a hierarchically structured attention
network is designed to simultaneously encode the intra-trajectory and
inter-trajectory dependencies, with the integration of the temporal attention
mechanism and global elastic attentional encoder. Extensive experiments
demonstrate the superiority of our AttnTUL method as compared to
state-of-the-art baselines on various trajectory datasets. The source code of
our model is available at https://anonymous.4open.science/r/Attn_TUL.

本文提出了一个新的分层时空注意力神经网络 ——AttnTUL，用于联合编码局部轨迹过渡模式和全局空间依赖性，以实现轨迹 - 用户链接，并使用各种轨迹数据集证明了其在性能上优于最先进的基线模型。

层次空间时间注意力网络下的轨迹 - 用户链接

Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks

The spread of COVID-19 leads to the global shutdown of many corporate
offices, and encourages companies to open more opportunities that allow
employees to work from a remote location. As the workplace type expands from
onsite offices to remote areas, an emerging challenge for an online hiring
marketplace is how these remote opportunities and user intentions to work
remotely can be modeled and matched without prior information. Despite the
unprecedented amount of remote jobs posted amid COVID-19, there is no existing
approach that can be directly applied.
Introducing a brand new workplace type naturally leads to the cold-start
problem, which is particularly more common for less active job seekers. It is
challenging, if not impossible, to onboard a new workplace type for any
predictive model if existing information sources can provide little information
related to a new category of jobs, including data from resumes and job
descriptions. Hence, in this work, we aim to propose a principled approach that
jointly models the remoteness of job seekers and job opportunities with limited
information, which also suffices the needs of web-scale applications. Existing
research on the emerging type of remote workplace mainly focuses on qualitative
studies, and classic predictive modeling approaches are inapplicable
considering the problem of cold-start and information scarcity. We precisely
try to close this gap with a novel graph neural architecture. Extensive
experiments on large-scale data from real-world applications have been
conducted to validate the superiority of the proposed approach over competitive
baselines. The improvement may translate to more rapid onboarding of the new
workplace type that can benefit job seekers who are interested in working
remotely.

本文介绍了一种基于图神经网络的原则性方法，该方法可以联合建模求职者和职位的远程程度，并解决信息不足的情况下怎样找到匹配的工作机会的问题，该方法已在真实世界应用的大规模数据上进行了广泛的实验，并验证了其在解决该领域的问题上的卓越性能。

强健多通道图神经网络在远程工作优化中的应用

Remote Work Optimization with Robust Multi-channel Graph Neural Networks

Accurate forecasting of citywide traffic flow has been playing critical role
in a variety of spatial-temporal mining applications, such as intelligent
traffic control and public risk assessment. While previous work has made
significant efforts to learn traffic temporal dynamics and spatial
dependencies, two key limitations exist in current models. First, only the
neighboring spatial correlations among adjacent regions are considered in most
existing methods, and the global inter-region dependency is ignored.
Additionally, these methods fail to encode the complex traffic transition
regularities exhibited with time-dependent and multi-resolution in nature. To
tackle these challenges, we develop a new traffic prediction
framework-Spatial-Temporal Graph Diffusion Network (ST-GDN). In particular,
ST-GDN is a hierarchically structured graph neural architecture which learns
not only the local region-wise geographical dependencies, but also the spatial
semantics from a global perspective. Furthermore, a multi-scale attention
network is developed to empower ST-GDN with the capability of capturing
multi-level temporal dynamics. Experiments on several real-life traffic
datasets demonstrate that ST-GDN outperforms different types of
state-of-the-art baselines. Source codes of implementations are available at
this https URL

为了更准确地预测城市交通流量并在智能交通管制和公共风险评估等方面实现更有效的空间 - 时间挖掘应用，我们开发了一种新的交通预测框架 Spatial-Temporal Graph Diffusion Network（ST-GDN），该框架具有层次结构的图神经架构，不仅可以从局部角度学习区域地理依赖关系，而且可以从全局角度学习空间语义。该框架还配备了多尺度注意力网络，以提高其捕捉多级时间动态的能力。多项实验结果表明 ST-GDN 的性能优于多种最新基线。