Graph-structured data appears in domains such as molecular analysis, social networks, and program optimization, where graphs often exhibit implicit heterogeneity, as nodes may look homogeneous in type yet differ significantly in semantics or functionality. Graph Neural Networks (GNNs), while powerful on homophilic graphs, tend to degrade in such settings due to polarity confusion, over-smoothing, and inefficiency caused by dense propagation. We propose a polarity-aware framework for graph classification that addresses these challenges through adaptive directional sparse aggregation. The framework introduces a polarity-aware propagation mechanism that adaptively reinforces or inverts neighbor signals, mitigating contamination under heterophily. A polarity-guided sparse aggregation operator further alleviates over-smoothing, improves scalability by constraining redundant connections, and condenses information flow into more effective representations, while maintaining unbiased estimation with controlled variance. We provide theoretical analyses that characterize the computational complexity, stability properties, and expressive behavior of signed directional aggregation, offering theoretical insights into its computational, stability, and expressive properties. Extensive experiments on molecular and social graph benchmarks with implicit heterophily demonstrate consistent improvements in graph classification accuracy and efficiency. Our method achieves a 2.36% improvement when compared with the strongest baseline on each dataset. In addition, it improves accuracy by 4.53% on average on program optimization strategy recognition tasks, reaching 80.12% overall.

Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while keeping the computational costs tractable. However, commonly used training data are independent and identically distributed, for inexact machine unlearning, current metrics are inadequate in quantifying unlearning degree that occurs after unlearning. To address this quantification issue, we introduce SPD-GAN, which subtly perturbs data distribution targeted for unlearning. Then, we evaluate unlearning degree by measuring the performance difference of the models on the perturbed unlearning data before and after unlearning. Furthermore, to demonstrate efficacy, we tackle the challenge of evaluating machine unlearning by assessing model generalization across unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. A by-product of our work is a novel method for evaluating and quantifying unlearning degree.

Federated Learning (FL) combined with Differential Privacy (DP) is widespread in healthcare, finance, and IoT due to its advantages in multi-client data distribution. However, existing FL approaches overlook the differential impact levels among clients and data redundancy issues, resulting in high computational overhead and limited real-time applicability. Additionally, non-independent identical distribution (Non-IID) and imbalanced datasets in multi-clients pose challenges in privacy preservation and model overfitting. Therefore, we propose a balanced weight strategy for multi-stage federated learning against multi-client data skewing, called BM-FL, which involves clients, intermediate trust servers (ITSs), and the central server (CS). Firstly, to protect data privacy, an improved Laplace$\epsilon$-differential privacy method is employed. Secondly, a novel generative adversarial network (GAN) called BC-GAN is introduced. It is used to generate realistic fake samples and maintain a balanced proportion of samples across different categories. Then, to make full use of each client's valuable data, we designe a balanced weight strategy. Moreover, extensive experimental results clearly demonstrate the effectiveness of BM-FL in efficiently handling classification tasks involving Non-IID and imbalanced datasets while maintaining privacy and security. Furthermore, our method attains superior classification accuracy with fewer training epochs compared to relevant classical algorithms. The code is available athttps://github.com/ylxzjy/BMFL.git.

As a fundamental problem in graph data mining, Densest Subgraph Discovery (DSD) aims to find the subgraph with the highest density from a graph. It has been studied for several decades and found a large number of real-world applications, such as network community detection, regulatory motif discovery in DNA, graph index construction, and fake follower detection. Although there are many existing DSD algorithms, they are often not scalable or efficient to process large-scale graphs, since most of them are serial algorithms and can only leverage the computing resource of a single CPU core. To tackle these issues, in this paper we propose efficient parallel algorithms for solving the DSD problems on both undirected and directed graphs at scale. Our main idea is to use the k-cores (a kind of dense subgraph) to approximate the densest subgraph in the undirected graphs, and then propose efficient parallel algorithms for computing the cores by optimizing the iterative process and also reducing the number of iterations. We further extend this idea for directed graphs by introducing a novel concept, named w-induced subgraph, to avoid unnecessary enumerations of x or y when searching [x,y]-cores (a kind of directed dense subgraph to approximate the densest). To verify the scalability and efficiency of the proposed algorithms, we have conducted extensive experiments on 12 large real-world graphs, and four of them are billion-scale. The experimental results show that our proposed algorithms outperform the state-of-the-art algorithms on both undirected and directed graphs, in terms of scalability and efficiency.

Graph convolutional network (GCN) has been extensively applied to recommender systems (RS) and achieved significant performance improvements through iteratively aggregating high-order neighbors to model the relevance between users and items as well as their characteristics. In the aggregation process, GCN models usually give neighbors the same or trainable weights based on implicit features, ignoring explicit ones. In this work, we take the features with explicit meanings or extracted with specific purpose as explicit ones (e.g., temporal features) and the others contained in user-item network as implicit ones (e.g., user preferences). However, some explicit features and knowledge play an essential role in improving the model representation ability and explainability in recommendation systems. To deal with the limitation, we propose a GCN based framework to embed the explicit features or those extracted with explicit intentions in this work. We also provide specific implementations based on two commonly researched features, temporal evolution and popularity bias. Specifically, we first experimentally analyze the popularity bias of the representation learning in RS based on two commonly used GCN models. Secondly, we propose a general framework to weigh neighbors based on explicit features or intentions. Thirdly, we implement a Temporal and Popularity weighted Aggregator (TPA) for GCN. The Interest-Forgetting Curve is utilized to capture temporal evolution as temporal weights and the data-driven Beta distribution is employed to tune the weights based on the node popularity flexibly. At last, we conduct extensive experiments on three real-world datasets to demonstrate the effectiveness of TPA in improving recommendation accuracy and alleviating the popularity bias.

Canonical Polyadic Decomposition (CPD) of sparse tensors is an effective tool in various machine learning and data analytics applications, in which sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is the major performance bottleneck. To overcome this bottleneck and support efficient applications, this paper presents HPSpTM, an efficient sparse MTTKRP framework, to exploit the multi-level parallelism and reusability on heterogeneous HPC systems. HPSpTM incorporates: (1) a multi-level matrix-driven tiling engine that leverages the process- and thread-level parallelism of the underlying platform and data reusability based on the derived factor matrix-driven MTTKRP algorithm; (2) a tensor-driven parallel execution that enables buffering-aware scheduling and pipeline scheduling to optimize the performance in the tile granularity; (3) a partition-aware light weight data storage that exploits better data locality based on the proposed hierarchical and fine-grained execution; and (4) a performance auto-tuning technique that offers large flexibility for tile size auto-adjusting across various input datasets based on a designed runtime model. Our experiments show that HPSpTM on a Nvidia Tesla P100 obtains the average performance improvement of up to 76.46% over the state-of-the-arts, and HPSpTM achieves the speedup of up to 15.39× when scaling from 8 to 128 core groups, corresponding to processes, on the Sunway TaihuLight supercomputer.

The excavated information will become obsolete when the data changes in dynamic graphs. To compute the up-to-date results, the graph algorithm has to re-compute the entire data from scratch, which will consume huge computation time and resources. To reduce the cost of such calculations, this paper proposes a model called IncGraph to support incremental iterative computation over dynamic graphs. Different from the way of traditional iteration, IncGraph executes the graph algorithm through reusing the results of the previous graph and performs computation on the part of the graph that has changed. IncGraph has two critical components: (1) an incremental iterative computation model that consists of two steps: an incremental step to calculate the results on the changed vertices of the graph, and a merge step to calculate the results on the entire graph by using the results of the previous graph and the incremental step; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. We implement IncGraph model on GraphX and evaluate its performance by using several representative iterative graph algorithms: PageRank, Connected components, and Single Source Shortest Path. The results show that compared with the traditional iteration, when adding the 100k of vertices in different size data sets, the performance optimization ratio of IncGraph is 31.79 percent averagely, and 50.2 percent maximum; and when the percentage of added vertices varied from 0.01 to 10 percent in different data sets, the performance optimization ratio of IncGraph varied from 19.9 to 66.1 percent. Moreover, the result errors of IncGraph is small and can be neglected.

MapReduce offers a promising programming model for big data processing. One significant issue in practical applications is data skew, its an important reason for the emergence of stragglers which makes the data assigned to each reducer imbalance. This paper presents CSRA, an efficient resource allocation algorithm in MapReduce considering data skew. CSRA aims at reducing the running time and coefficient of variation by reordering the task list and splitting the big clusters. Through thinking over the actual status of tasks, this method largely squares up the resource utilization. After we implement CSRA in Hadoop, the experiments show that CSRA has negligible overhead and can speed up the execution time of some popular applications obviously.