AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This study proposes an analytical class rebalancing method to compute the optimal rebalancing ratios for imbalanced datasets, and is the first to provide an analytical and parameter-free solution to the problem.
Source localization aims to locate the origin of failures and rumors in complex graphs, which has broad implications for financial systems. However, previous methods commonly assume that the propagation graph is known in advance. In practice, the graph on which failures or information propagate depends on relationships among assets and other factors that are difficult to observe. In this paper, we first construct a propagation proxy graph from the semantic similarity between financial posts by using a pretrained language model to encode their textual descriptions. We then design a two-stage framework for source localization based on a voting scheme. In the first stage, each activated node votes for the neighbor that may have triggered it. In the second stage, each node combines the votes received from its neighbors and determines whether it belongs to the source set. In the experiment, approximately 12,000 financial Twitter posts are encoded with a pretrained language model, and the propagation proxy graph is constructed by connecting the Top-k semantic neighbors of each node. The source-localization model is trained using susceptible–infected–recovered (SIR) trajectories and then applied to susceptible–infected (SI), independent cascade (IC), and linear threshold (LT) trajectories. This protocol simulates concept drift caused by a change in the underlying propagation mechanism. Across the three target diffusion settings, accuracy remains around 0.90, AUC exceeds 0.91, and macro-averaged precision ranges from 0.565 to 0.580. The experiments demonstrate that the proposed method remains effective when the diffusion mechanism changes after training.
Orthogonal Decomposition for Social Recommendation (ODSR) is proposed, an embedding-space framework that orthogonally decomposes the aggregated social message into an aligned component and an orthogonal deviation, and learns a dimension-wise vector gate to regulate the deviation under ranking supervision.
Class imbalance is a significant challenge in many practical classification tasks, particularly in federated learning (FL), where both global data imbalance and local data heterogeneity across clients worsen the problem. Addressing the class imbalance problem effectively while adhering to privacy constraints remains a formidable challenge. A common solution is to rebalance (or reweight) the samples of different classes; however, the rebalancing ratio largely depends on empirical results. In this study, we propose an analytical class rebalancing method to compute the optimal rebalancing ratios for imbalanced datasets. We first theoretically derive the relationship between evaluation metrics--such as macro-precision, macro-recall, and macro-F1--and the rebalancing ratio. Based on these findings, we devise an efficient algorithm to determine the optimal rebalancing ratio that maximizes the corresponding metrics. Our method is parameter-free and doesn't increase the complexity of existing neural models. We demonstrate that our method achieves the optimum ratio for maximizing the concerned metrics while maintaining low computational complexity, scaling linearly with both the number of clients and samples. Experimental results on different datasets validate the effectiveness of our algorithms. To the best of our knowledge, we are the first to provide an analytical and parameter-free solution to the problem.
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