Chuanwen Li

Graphs are widely used to represent vertices and their relationships, with the subgraph counting problem being a crucial challenge in network analysis. Originating from subgraph matching, this problem aims to efficiently determine the number of subgraphs in a data graph that are isomorphic to a given query graph. Since this task is NP-hard, existing approaches are typically divided into exact and approximate methods. Exact methods compute the precise number of isomorphic subgraphs but incur prohibitive computational costs on large graphs. Approximate methods, often based on Graph Neural Networks (GNNs), estimate the subgraph count more efficiently but tend to suffer when there is a large size discrepancy between the data graph and the query graph, leading to inaccurate vertex matching. To address these limitations, we propose a domination-based subgraph counting method (DSCM), which improves vertex matching by using a vertex dominance embedding method. This approach, combined with an attention mechanism, accurately computes subgraph counts. Experimental results show that DSCM outperforms existing methods, providing an efficient solution for subgraph counting in large graphs.

Harmonic drive (HD), as an essential component of industrial robots, is susceptible to damage due to product manufacturing and working conditions. Therefore, it is imperative to conduct a comprehensive test to accurately diagnose potential HD faults. In this paper, a SPD Manifold-based Graph Neural Network (SPD-GNN) is proposed to accurately identify different faults of HD. In the time-frequency domain, the high-definition vibration signals obtained from multiple sensors are segmented. The resulting multi-channel spatial covariance matrices (MSCM) act as vertices in a time-frequency graph. A SPD-GNN is then utilized to extract classification details while preserving discriminative capabilities. Subsequently, the SPD matrices are projected to the tangent space using a logarithmic mapping (LOG) layer. Ultimately, the data is input into a cross-entropy loss function for subsequent computation. The SPD-GNN uses graph convolutional techniques tailored for SPD matrices to capture HD features in the time-frequency domain.

Federated learning, which allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data, is an emerging distributed learning technique due to its nature of privacy preservation. However, it has been found that models trained with federated learning usually have worse performance than their counterparts trained in the standard centralized learning mode, especially when the training data is imbalanced. In the context of federated learning, data imbalance may occur either locally one one client device, or globally across many devices. The complexity of different types of data imbalance has posed challenges to the development of federated learning technique, especially considering the need of relieving data imbalance issue and preserving data privacy at the same time. Therefore, in the literature, many attempts have been made to handle class imbalance in federated learning. In this paper, we present a detailed review of recent advancements along this line. We first introduce various types of class imbalance in federated learning, after which we review existing methods for estimating the extent of class imbalance without the need of knowing the actual data to preserve data privacy. After that, we discuss existing methods for handling class imbalance in FL, where the advantages and disadvantages of the these approaches are discussed. We also summarize common evaluation metrics for class imbalanced tasks, and point out potential future directions.