Aggregating noisy labels from mobile crowdsourcing (MCS) to recover true labels is a fundamental yet challenging problem, especially due to the sparsity and unreliability of crowd-contributed data. While most prior work addresses only single-label scenarios, real-world MCS applications often require robust solutions for both single-label and multi-label tasks, where each instance may be associated with multiple categories. In this paper, we propose ATHENA, a novel approach that leverages self-supervision signals inherent in MCS data for effective label aggregation. Firstly, we propose a graph transformer model that can learn from the MCS topology and features. Then, we propose self-supervision signals inherently included in the dataset to help aggregate the labels. To address the unique challenges of multi-label aggregation, we further extend our approach toATHENA+, introducing a label message passing (LMP) module that explicitly models correlations and dependencies among labels. We conducted extensive experiments on multiple single-label and multi-label classification datasets, comparing the proposed models with state-of-the-art methods. Our results demonstrate that ATHENA and ATHENA+ are highly effective in aggregating labels and obtain much better performance than existing methods.

The proliferation of smart mobile devices has catalyzed the growth of Mobile CrowdSourcing (MCS) as a distributed problem-solving paradigm. MCS platforms heavily rely on advanced truth inference techniques to extract reliable information from diverse and potentially noisy crowd-contributed data. Existing truth inference models often made simplified assumptions about workers or tasks, employing complex Bayesian models or stringent data aggregation methods. These approaches tend to be task-specific, primarily limited to categorical labeling, making adaptations to other mobile computing scenarios labor-intensive. To address these limitations, we introduce the Bipartite Attention-driven Truth (BAT), a versatile approach tailored for mobile computing environments. BAT utilizes an Attributed Bipartite Graph (ABG) to holistically model the MCS process, with workers and tasks as nodes connected by edges representing answer-specific attributes. The approach employs a bipartite graph neural network with an innovative attention mechanism to assess the importance of different answers. BAT extends beyond categorical tasks to support numerical ones by incorporating novel feature representations and model extensions. Theoretical analyses clarify the link between answer similarity and worker expertise. Extensive experiments using diverse real-world datasets demonstrate BAT's superior performance compared to state-of-the-art categorical and numerical truth inference models, highlighting its effectiveness in mobile computing scenarios.

Changes in enterprise networks require updated configurations. However, manual configurations with slow update efficiency, poor performance, and handling limitations, lead to the unavailability of updated networks. Therefore, we propose an efficient network renascence framework, NetRen, which synthesizes OSPF/BGP configurations driven by service and traffic migration. We follow the workflow of sketch extraction, configuration synthesis, and repair. Initially, comprehensive graphs are constructed to represent configuration sketches. We propose a GraphTrans synthesizer with Transformer's benefits of long-range focus and parallel reasoning. Training samples with the optimization relationship enable the synthesizer to achieve a mapping that optimizes performance based on configurations. To overcome the satisfiability barrier, configurations from the synthesizer are input to the stepwise configuration repairer as well-initialized solutions, achieving rapid configuration repair. Experiments demonstrate that the consistency of network configurations output by the GraphTrans synthesizer averages 98%. NetRen achieves a 312.4× increase in synthesis efficiency and a 5.83% improvement in network performance.

Existing peer-to-peer data-sharing methods suffer from low data delivery efficiency and scalability due to the naive data request/response procedure and the high redundant data transmission rate. It becomes even worse in large-scale mobile networks considering the limited resources of mobile devices. To address this issue, this paper presents MobiShare, an efficient decentralized data-sharing approach for mobile devices, which allows users to not only share the data but also the data generation methods. To achieve MobiShare, we introduce a function block encoding method and a data request method to enhance sharing efficiency, minimizing costs for decentralized data sharing. We propose a credit payment mechanism where congested devices can send data vouchers instead of actual data, containing the expected transmission time. Based on the load and bandwidth of devices, we build the optimized dissemination tree with data vouchers in a decentralized way to improve scalability. Evaluation results show that MobiShare avoids redundant transmission. It greatly shortens transmission completion time and lowers energy consumption with limited network resources.

Desirable information processing in space-terrestrial integrated vehicle networks (STINs) handles data distributed in different satellites while transmitting, where efficient modeling time-varying resources is critical. Existing works are not applicable to STINs, however, because they lack the joint consideration of different movement patterns and fluctuating loads. In this paper, we propose theTime-Varying Resource Graph (TVRG)to model dynamic resources in STINs, by leveraging the advantages of software-defined networking in flexible resource management. Firstly, we propose theSTIN mobility modelto uniformly model different movement patterns in STINs. Then, we propose alayered Resource Modeling and Abstraction (RMA)approach, where evolutions of node resources are modeled as Markov processes, by encoding predictable topologies and influences of fluctuating loads as states. Besides, we propose the low-complexity domain resource abstraction algorithm by defining two mobility-based and load-aware partial orders on resource abilities. Finally, we formulate theTVRG-based Processing on the Way (TPoW)problem for data flows with processing requirements and multiple sources. We propose aMulti-level Processing on the Way (MPoW)approach with a bounded approximation ratio, realizing adaptive matching of resources and demands of processing and transmission. To evaluate the RMA approach, we propose aTVRG-based Routing (TR)algorithm for time-sensitive and bandwidth-intensive data flows, with the multi-level on-demand scheduling ability. Comprehensive simulation results demonstrate that our RMA-TR and MPoW outperform most related schemes by decreasing nearly 40% bandwidth consumption with the shortest end-to-end delay.

Performance analysis is of great importance for management and optimization of space-terrestrial integrated networks (STINs). Traditional approaches to network performance analysis are often based on idealized assumptions that are deviated from the real network environment. This leads to the fact that these models are usually inefficient and restricted in real-world STINs with complicated behavior and even dynamic capacity. In this paper, we propose a network performance analysis approach EAGLE based on heterogeneous graph neural networks. Firstly, we propose a powerful computer network representation model that can preserve all of the information in computer networks. It represents different components of computer networks as a set of heterogeneous nodes and edges, and finally constructs a heterogeneous graph. Then, we obtain the topological representation for the routers in the network through a bandwidth-aware network embedding model. Based on this heterogeneous graph, we propose a heterogeneous GNN model to accurately predict network KPIs because it can completely capture the rich topological and attribute information of computer networks. Experimental results demonstrate that EAGLE can accurately model different networks, and outperforms both traditional methods and the latest neural network-based methods.