In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck principle to learn common information among different views in a self-supervised manner, and accordingly establish a new framework called Self-supervised Information Bottleneck based Multi-view Subspace Clustering (SIB-MSC). Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views. Actually, the latent representation of each view provides a kind of self-supervised signal for training the latent representations of other views. Moreover, SIB-MSC attempts to disengage the other latent space for each view to capture the view-specific information by introducing mutual information based regularization terms, so as to further improve the performance of multi-view subspace clustering. Extensive experiments on real-world multi-view data demonstrate that our method achieves superior performance over the related state-of-the-art methods.

Unmanned aerial vehicle (UAV) crowdsensing (UCS) is an emerging data collection paradigm to provide reliable and high quality urban sensing services, with age-of-information (AoI) requirement to measure data freshness in real-time applications. In this paper, we explicitly consider the case to ensure that the attained AoI always stay within a specific threshold. The goal is to maximize the total amount of collected data from diverse Point-of-Interests (PoIs) while minimizing AoI and AoI threshold violation ratio under limited energy supplement. To this end, we propose a decentralized multi-agent deep reinforcement learning framework called “DRL-UCS( $\text{AoI}_{th}$ )” for multi-UAV trajectory planning, which consists of a novel transformer-enhanced distributed architecture and an adaptive intrinsic reward mechanism for spatial cooperation and exploration. Extensive results and trajectory visualization on two real-world datasets in Beijing and San Francisco show that, DRL-UCS( $\text{AoI}_{th}$ ) consistently outperforms all nine baselines when varying the number of UAVs, AoI threshold and generated data amount in a timeslot.

Graph structured data often possess dynamic characters in nature, such as the addition of links and nodes, in many real-world applications. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling graph data. However, almost all existing approaches operate under the assumption that, upon the establishment of a new link, the embeddings of the neighboring nodes should undergo updates to learn temporal dynamics. Nevertheless, these approaches face the following limitation: If the node introduced by a new connection contains noisy information, propagating its knowledge to other nodes becomes unreliable and may even lead to the collapse of the model. In this paper, we propose Ada-DyGNN : a robust knowledge Ada ptation framework via reinforcement learning for Dy namic G raph N eural N etworks. In contrast to previous approaches, which update the embeddings of the neighbor nodes immediately after adding a new link, Ada-DyGNN adaptively determines which nodes should be updated. Considering that the decision to update the embedding of one neighbor node can significantly impact other neighbor nodes, we conceptualize the node update selection as a sequence decision problem and employ reinforcement learning to address it effectively. By this means, we can adaptively propagate knowledge to other nodes for learning robust node embedding representations. To the best of our knowledge, our approach constitutes the first attempt to explore robust knowledge adaptation via reinforcement learning specifically tailored for dynamic graph neural networks. Extensive experiments on three benchmark datasets demonstrate that Ada-DyGNN achieves the state-of-the-art performance. In addition, we conduct experiments by introducing different degrees of noise into the dataset, quantitatively and qualitatively illustrating the robustness of Ada-DyGNN.

Discovering tables from poorly maintained data lakes is a significant challenge in data management. Two key tasks are identifying joinable and unionable tables, crucial for data integration, analysis, and machine learning. However, there's a lack of a comprehensive benchmark for evaluating existing methods. To address this, we introduce LakeBench, a large-scale table discovery benchmark. It evaluates effectiveness, efficiency, and scalability of table join & union search methods. With over 16 million real tables, LakeBench is 1,600X larger than existing datasets and 100X larger in storage size. It includes synthesized and real queries with ground truth, totaling more than 10 thousand queries - 10X more than used in any existing evaluation. We spent over 7,500 human hours labeling these queries and constructing diverse query categories for thorough evaluation. Our benchmark thoroughly evaluates state-of-the-art table discovery methods, providing insights into their performance and highlighting research opportunities.

Multimodal path queries over transportation networks are receiving increasing attention due to their widespread applications. A multimodal path query consists of finding multimodal journeys from source to destination in transportation networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. Transportation networks are generally continent-sized. This characteristic highlights the need for parallel computing to accelerate multimodal path queries. Meanwhile, transportation networks are often fragmented and distributively stored on different machines. This situation calls for exploiting parallel computing power for these distributed systems. Therefore, in this paper, we study distributed multimodal path (DMP) queries over large transportation networks. We develop algorithms to explore parallel computation. When evaluating a DMP query Q on a distributed multimodal graph Gmult, we show that the algorithms possess the following performance guarantees, irrespective of how Gmult is fragmented and distributed: (1) each machine is visited only once; (2) the total network traffic is determined by the size of Q and the fragmentation of Gmult, independent of the size of Gmult; (3) the response time is decided by the largest fragment of Gmult; and (4) the algorithm is parallel scalable. Using real-life and synthetic data, we experimentally verify that the algorithms are scalable on large graphs.