In recent years, graph-partition-based clustering algorithms have attracted increasing attention. These algorithms first construct a graph over the data points and then partition this graph, regarding each connected subgraph in the partitioned graph as a cluster. However, traditional graph-partition-based clustering algorithms face challenges when clustering datasets with highly imbalanced density distributions. This is because, during the process of graph partitioning, they mainly rely on edge lengths and largely ignore local density variations. To address this issue, we propose the Adaptive Graph Partitioning (AGP) clustering algorithm. AGP integrates local density information into the partitioning process and adaptively normalizes the magnitudes of edge weights in both sparse and dense regions. This enhancement allows AGP to avoid the over-partitioning of dense clusters, effectively addressing a common problem in traditional graph-partition-based clustering algorithms. Additionally, the mechanism of connectivity domain differences is introduced into AGP, further enhancing the algorithm’s ability to discriminate between neighboring clusters that are difficult to separate. Extensive experiments on 13 benchmark datasets show that AGP achieves the best clustering performance on 7 datasets and performs competitively on the others, especially when density imbalance is severe. Moreover, scalability experiments on datasets with up to 100,000 data points, together with complexity analysis, demonstrate that AGP enjoys favorable time and memory efficiency compared with representative baselines.

Bayesian networks are of paramount significance in modeling joint probability distributions and have garnered extensive applications across diverse domains. The continuous optimization method formulates the structure learning problem as a purely continuous optimization problem within the space of real matrices, thereby offering a novel avenue for learning directed acyclic graphs (DAGs). In our quest to enhance performance and interpretability, we introduce a groundbreaking continuous optimization approach for learning the structures of DAGs, namely DAGs structure learning with Absorbing Markov Chain (DAG-AMC). DAG-AMC ingeniously transforms the acyclic constraint into a node transition challenge, effectively recasting it as a Markov chain problem with absorbing states. This innovative transformation reconceptualizes the graph structure as a state transition matrix within the framework of an absorbing Markov chain. The absorption time intrinsic to this chain provides an elegant representation of the acyclic constraint in DAG structure learning. We leverage the augmented Lagrangian method, incorporating the constructed smooth function as a constraint throughout the optimization process. Empirical experiments conducted on both synthetic and real-world datasets highlight the remarkable efficacy of our proposed DAG-AMC. Our results consistently surpass those of baseline methods across a wide array of evaluation metrics, thus underscoring the superior potential of DAG-AMC as a preeminent solution for DAG structure learning.

Intelligent logistics relies on accurately predicting the service time, which is a part of time cost in the last-mile delivery. However, service time prediction (STP) is non-trivial given complex delivery circumstances, location heterogeneity, and skewed observations in space, which are not well-handled by existing solutions. In our prior work, we treat STP at each location as a learning task to keep the location heterogeneity, propose a prior knowledge-enhanced meta-learning to tackle skewed observations, and introduce a Transformer-based representation module to encode complex delivery circumstances. Maintaining the design principles of prior work, in this extended paper, we propose MetaSTP+. In addition to fusing the prior knowledge after the meta-learning process, MetaSTP+also injects the prior knowledge before and during the meta-learning process to better tackle skewed observations. More specifically, MetaSTP+completes the support set of tasks with scarce samples from other tasks based on prior knowledge and is equipped with a prior knowledge-aware historical observation encoding module to achieve those purposes accordingly. Experiments show MetaSTP+outperforms the best baseline by 11.2% and 8.4% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP+is deployed in JD Logistics.

Urban sensing aims to sense the status of the city, e.g., air quality, noise level, concentration of viruses, which can be completed by spatial crowdsourcing. Multi-destination people, who have many intermediate locations to visit before the final destination, e.g., couriers and tourists, are ideal recruitment candidates to conduct sensing tasks since they spend more time outside and have a wide spatio-temporal distribution. However, existing spatial crowdsourcing methods are only designed for workers who have single destinations, e.g., commuters, which are not applicable to recruit the multiple-destination people. Therefore, in this paper, we generalize the urban crowdsensing problem to the multi-destination scenario, namely, Urban Sensing for Multi-Destination Workers (USMDW). We prove its NP-hardness, and propose a framework Urban Sensing for Multi-destination Workers via Deep REinforcement learning, i.e., SMORE, to solve it effectively and efficiently. SMORE is composed of two steps: 1) candidate assignment initialization, which initializes all feasible sensing task-worker assignment pairs by a pre-trained reinforcement learning-based working route planning solver; and 2) reinforcement learning-based iterative selection, which iteratively selects a sensing task-worker pair to the current assignment via a novel policy network, i.e., Two-stage Assignment Selection Network (TASNet). Extensive experiments on three real-world datasets show SMORE outperforms the best baseline in data coverage by 5.2% on average with high efficiency.

Crime risk map plays a crucial role in urban planning and public security management. Traditionally, it is obtained solely from historical crime incidents or inferred from limited environmental factors, which are not sufficient to accurately model the occurrences of crimes over the geographical space well. Motivated by the impressive and realistic conditional generating power of diffusion models, in this paper, we propose a multimodal conditional diffusion method, namely, DiffCrime, to infer the crime risk map based on datasets in various domains, i.e., historical crime incidents, satellite imagery, and map imagery. It is equipped with a history-gated multimodal denoising network, i.e., HamNet, dedicated to the crime risk map inference. HamNet emphasizes the importance of historical crime data via a Gated-based History Fusion (GHF) module and adaptively controls multimodal conditions to be fused across different diffusion time steps via a Time step-Aware Modality Fusion (TAMF) module. Extensive experiments on two real-world datasets demonstrate the effectiveness of DiffCrime, which outperforms baselines by at least 43% and 31% in terms of RMSE, respectively.

Discovering causal relationships among observed variables is an important research focus in data mining. Existing causal discovery approaches are mainly based on constraint-based methods and functional causal models (FCMs). However, the constraint-based method cannot identify the Markov equivalence class and the functional causal models cannot identify the complex interrelationships when multiple variables affect one variable. To address the two aforementioned problems, we propose a new graph structure Causal Star Graph (CSG) and a corresponding framework Causal Discovery via Causal Star Graphs (CD-CSG) to divide a causal directed acyclic graph into multiple CSGs for causal discovery. In this framework, we also propose a generalized learning in CSGs based on a variational approach to learn the representative intermediate variable of CSG’s non-central variables. Through the generalized learning in CSGs, the asymmetry in the forward and backward model of CD-CSG can be found to identify the causal directions in the directed acyclic graphs. We further divide the CSGs into three categories and provide the causal identification principle under each category in our proposed framework. Experiments using synthetic data show that the causal relationships between variables can be effectively identified with CD-CSG and the accuracy of CD-CSG is higher than the best existing model. By applying CD-CSG to real-world data, our proposed method can greatly augment the applicability and effectiveness of causal discovery.

Discovering causal relationships among observed variables is a new research focus in the area of data mining. Methods based on the additive noise model have been proved to be efficient in the identification of cause-effect pairs. However, when trying to determine many-to-one causality, additive noise models often fail to identify the causal direction due to the complex interrelationships and interactions even though the generation of each causal relation follows the additive noise model, and become unreliable in practical applications. In this work, to identify the causal direction, we propose a Hierarchical Additive Noise Model (HANM) to convert many-to-one causality into an approximate one-to-one causality by generalizing multiple factors into an intermediate variable with a variational approach, and use asymmetry in the forward model and backward model of HANM to identify causal direction. Experiments using synthetic data show that many-to-one causality can be effectively identified through asymmetry with our proposed HANM and the accuracy of HANM is higher than the best existing model. By applying the model to real-world data, it can be seen that HANM can greatly augment the application scope of functional causal models for causal discovery.