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The theoretical analysis proves that CNDGNN performs better than GCN in the conflicting nodes and explores the key design view when solving the conflicting node problem.
Database Operations and Maintenance (O&M) is a critical but complex and labor-intensive task. Recent LLM-based assistants promise to lower the barrier by reading manuals/tickets and exploring diagnostic search trees. However, existing LLM-based solutions fall short due to fundamental limitations in learning from expert demonstration. Such design fails to internalize domain dynamics (how interventions change plans, resources, etc.), and struggles under workload and statistics drift. To address this, we present DBAgent, an autonomous agent for Huawei Cloud Data Warehouse Service (DWS) integrated with Autopilot (DWS's production monitoring, alerting, and auto-remediation service). DBAgent consumes DWS telemetry (e.g., KPIs and execution plans) and Autopilot alerts to diagnose and remediate incidents in production clusters. DBAgent emulates an expert's iterative Think-Act-Observe problem-solving loop with a policy trained via reinforcement learning (RL). It couples dynamic tool use for information gathering, a multimodal perception module for database-native signals, and an RL-based reasoning engine that plans, verifies, and generates evidence-grounded remediation recommendations. Extensive experiments show that DBAgent handles a broad range of complex O&M tasks. It surpasses the strongest baseline by +23% success rate and produces evidence-grounded reports with low hallucination (~5%) on DWS benchmark.
Graph Neural Networks (GNNs) have exhibited extraordinary performance in data mining. The success depends on the message passing mechanism, which is consistent with the homophily assumption that adjacent nodes share similar behaviors. However, the connected nodes in real-world graphs often show dissimilar behaviors, which is presented as the heterophily problem. A series of works proposed various models to solve the heterophily problem. They usually assume that the neighborhood distribution of nodes with the same class tends to be similar, while distinguishable from nodes of different classes. However, our experiments discovered that many nodes belonging to different classes share the same neighbor distribution in the mainstream datasets. The performance of GNN also degrades in these conflicting nodes. To detect the conflicting nodes and improve their prediction accuracy, we propose the Conflicting Node Discrimination GNN (CNDGNN). It firstly detects the possible conflicting nodes with the same neighbor distribution according to the selected high-quality pseudo-label. Then it estimates the class type of conflicting nodes with the same neighbor distribution through nodes' high-order neighbor distributions and original features, and the aggregation weights are learned to assign positive weights for nodes of the same class type and negative weights for nodes of different classes. The theoretical analysis proves that CNDGNN performs better than GCN in the conflicting nodes and explores the key design view when solving the conflicting node problem. The experiments demonstrate the effectiveness of our method in the mainstream datasets and verify that our method performs better in conflicting nodes.
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