Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.

Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due to the discrete and high-dimensional nature of graphs, as well as permutation invariance to node orderings in underlying graph distributions. Current leading autoregressive models fail to capture the permutation invariance nature of graphs for the reliance on generation ordering and have high time complexity. Here, we propose a continuous-time generative diffusion process for permutation invariant graph generation to mitigate these issues. Specifically, we first construct a forward diffusion process defined by a stochastic differential equation (SDE), which smoothly converts graphs within the complex distribution to random graphs that follow a known edge probability. Solving the corresponding reverse-time SDE, graphs can be generated from newly sampled random graphs. To facilitate the reverse-time SDE, we newly design a position-enhanced graph score network, capturing the evolving structure and position information from perturbed graphs for permutation equivariant score estimation. Under the evaluation of comprehensive metrics, our proposed generative diffusion process achieves competitive performance in graph distribution learning. Experimental results also show that GraphGDP can generate high-quality graphs in only 24 function evaluations, much faster than previous autoregressive models.

The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive. Deep generative learning gives us hope that we can automate this planning process and come up with the ideal urban plans. While remarkable achievements have been obtained, they have exhibited limitations in lacking awareness of: 1) the hierarchical dependencies between functional zones and spatial grids; 2) the peer dependencies among functional zones; and 3) human regulations to ensure the usability of generated configurations. To address these limitations, we develop a novel human-instructed deep hierarchical generative model. We rethink the urban planning generative task from a unique functionality perspective, where we summarize planning requirements into different functionality projections for better urban plan generation. To this end, we develop a three-stage generation process from a target area to zones to grids. The first stage is to label the grids of a target area with latent functionalities to discover functional zones. The second stage is to perceive the planning requirements to form urban functionality projections. We propose a novel module: functionalizer to project the embedding of human instructions and geospatial contexts to the zone-level plan to obtain such projections. Each projection includes the information of land-use portfolios and the structural dependencies across spatial grids in terms of a specific urban function. The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations. Finally, we present extensive experiments to demonstrate the effectiveness of our framework.

传统的城市规划要求城市专家在许多建筑限制下花费大量时间和精力制定最佳的城市计划。深层生成学习的非凡富有想象力为翻新城市规划提供了希望。尽管已经检查了自动化的城市规划师，但由于以下情况，它们受到限制：1）忽略人类在城市规划中的要求； 2）省略城市规划中的空间层次结构，以及3）缺乏许多城市计划数据样本。为了克服这些局限性，我们提出了一个新颖的，深厚的人类建筑的城市规划师。在初步工作中，我们将其提出为编码器范式。编码器是学习周围环境，人类指示和土地使用配置的信息分布。解码器是重建土地使用配置和相关的城市功能区域。重建过程将捕获功能区和空间网格之间的空间层次结构。同时，我们引入了一种变异的高斯机制来减轻数据稀疏问题。即使早期的工作导致了良好的结果，但生成的性能仍然不稳定，因为捕获空间层次结构的方式可能会导致不清楚的优化方向。在此期刊版本中，我们提出了一个基于生成的对抗网络（GAN）的层叠的深层生成框架，以解决此问题，灵感来自城市专家的工作流程。特别是，第一个gan的目的是根据人类指示和周围环境的信息来建立城市功能区域。第二个GAN将基于已构造的功能区域产生土地使用构型。此外，我们为增强数据样本提供了调节增强模块。最后，我们进行了广泛的实验以验证工作的功效。

深层神经网络预测交通需求已引起了学术界和行业社会的广泛兴趣。其中，成对来源点（OD）需求预测是一个有价值但具有挑战性的问题：（i）大量可能的OD对，（ii）空间依赖性的内在性和（iii）交通的复杂性状态。为了解决上述问题，本文提出了一种连续的时间和多级动态图表表示方法，用于原始用途需求预测（CMOD）。首先，构建了一个连续的动态图表示学习框架，该框架维护每个流量节点（地铁站或出租车区）的动态状态向量。国家向量保留历史交易信息，并根据最近发生的交易不断更新。其次，提出了多层结构学习模块，以模拟站点级节点的空间依赖性。它不仅可以从数据自适应地利用节点之间的关系，还可以通过集群级别和区域级虚拟节点共享消息和表示形式。最后，跨级融合模块旨在集成多级记忆并为最终预测生成综合节点表示。在北京地铁和纽约出租车的两个现实世界数据集上进行了广泛的实验，结果证明了我们的模型与最先进的方法相比。

最近的研究表明，在将图神经网络应用于多元时间序列预测中，其中时间序列的相互作用被描述为图形结构，并且变量表示为图节点。沿着这一行，现有方法通常假定确定图神经网络的聚合方式的图形结构（或邻接矩阵）是根据定义或自学来固定的。但是，变量的相互作用在现实情况下可以是动态的和进化的。此外，如果在不同的时间尺度上观察到时间序列的相互作用序列的相互作用大不相同。为了使图形神经网络具有灵活而实用的图结构，在本文中，我们研究了如何对时间序列的进化和多尺度相互作用进行建模。特别是，我们首先提供与扩张的卷积配合的层次图结构，以捕获时间序列之间的比例特定相关性。然后，以经常性的方式构建了一系列邻接矩阵，以表示每一层的不断发展的相关性。此外，提供了一个统一的神经网络来集成上述组件以获得最终预测。这样，我们可以同时捕获成对的相关性和时间依赖性。最后，对单步和多步骤预测任务的实验证明了我们方法比最新方法的优越性。

Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known labels for computing the classification loss at the output. In recent years, several methods have been designed to additionally utilize the labels at the input. One part of the methods augment the node features via concatenating or adding them with the one-hot encodings of labels, while other methods optimize the graph structure by assuming neighboring nodes tend to have the same label. To bring into full play the rich information of labels, in this paper, we present a label-enhanced learning framework for GNNs, which first models each label as a virtual center for intra-class nodes and then jointly learns the representations of both nodes and labels. Our approach could not only smooth the representations of nodes belonging to the same class, but also explicitly encode the label semantics into the learning process of GNNs. Moreover, a training node selection technique is provided to eliminate the potential label leakage issue and guarantee the model generalization ability. Finally, an adaptive self-training strategy is proposed to iteratively enlarge the training set with more reliable pseudo labels and distinguish the importance of each pseudo-labeled node during the model training process. Experimental results on both real-world and synthetic datasets demonstrate our approach can not only consistently outperform the state-of-the-arts, but also effectively smooth the representations of intra-class nodes.

Hierarchical text classification aims to leverage label hierarchy in multi-label text classification. Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels. Since global hierarchy is static and irrelevant to text samples, it makes these methods hard to exploit hierarchical information. Contrary to global hierarchy, local hierarchy as a structured labels hierarchy corresponding to each text sample. It is dynamic and relevant to text samples, which is ignored in previous methods. To exploit global and local hierarchies,we propose Hierarchy-guided BERT with Global and Local hierarchies (HBGL), which utilizes the large-scale parameters and prior language knowledge of BERT to model both global and local hierarchies.Moreover,HBGL avoids the intentional fusion of semantic and hierarchical modules by directly modeling semantic and hierarchical information with BERT.Compared with the state-of-the-art method HGCLR,our method achieves significant improvement on three benchmark datasets.

给定一系列集合，其中每个集合与时间戳关联并包含任意数量的元素，时间集的任务预测旨在预测后续集合中的元素。先前对时间集预测的研究主要通过从自己的序列中学习来捕获每个用户的进化偏好。尽管有见地，但我们认为：1）不同用户序列中潜在的协作信号是必不可少的，但尚未被利用； 2）用户还倾向于显示固定的偏好，而现有方法未能考虑。为此，我们提出了一个集成的学习框架，以对时间集预测的用户的进化和固定偏好进行建模，该预测首先通过按时间顺序排列所有用户群的交互来构建通用序列，然后在每个用户集中学习相互作用。特别是，对于每个用户集的交互，我们首先设计一个进化用户偏好建模组件，以跟踪用户的时间不断发展的偏好，并在不同用户之间利用潜在的协作信号。该组件维护一个存储库来存储相关用户和元素的记忆，并根据当前编码的消息和过去的记忆不断更新其记忆。然后，我们设计了一个固定的用户偏好模型模块，以根据历史序列来发现每个用户的个性化特征，该模块从双重角度自适应地汇总了以前相互作用的元素，并在用户和元素的嵌入方式的指导下。最后，我们开发了一种设定批次算法来提高模型效率，该算法可以提前创建时间一致的批次，并平均实现3.5倍的训练速度。现实世界数据集的实验证明了我们方法的有效性和良好的解释性。

对话摘要已被广泛研究和应用，其中，先前的作品主要集中在探索卓越的模型结构方面，以对准输入对话和输出摘要。然而，对于专业对话（例如，法律辩论和医学诊断），语义/统计对齐可能几乎不会填补输入对话话语话语和外部知识的摘要输出之间的逻辑/事实差距。在本文中，我们主要研究了非预介绍和预用环境下对话检验摘要（DIS）的事实不一致问题。创新的端到端对话摘要生成框架是有两个辅助任务：预期事实方面正规化（EFAR）和缺少事实实体歧视（MFED）。综合实验表明，该模型可以以准确的事实方面的覆盖率来产生更可读的总结，以及通知用户从输入对话中检测到的潜在缺失事实以获得进一步的人为干预。