Dynamic networks are data structures that represent the interactions among various entities in real-world systems, with their topology and node properties evolving over time. However, prevailing approaches typically derive node embeddings through aggregating temporal neighbor nodes of adjacent several hops, thus failing to capture the long temporal dependencies in dynamic networks. Furthermore, existing research on dynamic networks focuses on node- and edge-level tasks, lacking the support of graph-level tasks. To address the limitations of current approaches, this paper proposes TP-GNN, a novel continuous dynamic graph neural network model intended for graph classification in dynamic networks, which offers two primary advantages: (1) TP-GNN captures the long temporal dependencies via a novel message-passing method based on the information flow among the nodes, and (2) it learns the network evolution process from edge order for accurate dynamic network analytics. We evaluate the performance of TP-GNN in five datasets, including a new dataset we created from a Java software project. The results show that our method outperforms state-of-the-art approaches in graph classification with an average improvement of 4.91% in terms of$F_{1}$Score11Codes and dataset are available at https://github.com/Jie-0828/TP-GNN..

System logs, recording critical information about system operations, serve as indispensable tools for system anomaly detection. Graph-based methods have demonstrated superior performance compared to other methods in capturing the interdependencies of log events. However, existing methods often neglect the complex substructure patterns of nodes within log graphs, making it challenging to capture the subtle alteration in event type, structure, and the location of exceptions that indicate node anomalies. To address this limitation, this paper proposes a novel framework called Substructure-aware Log Anomaly Detection at Code File Level (SLAD). It first introduces a Monte Carlo Tree Search strategy tailored specifically for log anomaly detection to discover representative substructures. Then, SLAD incorporates a substructure distillation way to enhance the efficiency of anomaly inference based on the representative substructures. After that, we introduce a soft pruning to obtain key substructure for nodes. Experimental results show SLAD outperforms all baselines. Particularly, SLAD demonstrates at least 15 times faster than substructure-based graph learning methods in anomaly inference.

Anomaly detection is an important task that improves the maturity and stability of a software during its development. System logs record rich information about the running states of the software and reveal key insights of anomalous behaviors. This paper addresses anomaly detection using system log data and aims to resolve two challenges: First, different from most existing supervised learning-based anomaly detection methods that rely heavily on expensive, manually-curated labels, we aim to design an algorithm to make the most of scarce label information. Second, as a typical software system would contain very few anomalies, we aim to address the data imbalance issue which is often overlooked by existing studies. To address the challenges above, we propose LGLog, a semi-supervised anomaly detection framework that is based on system logs. First, LGLog transforms log sequences into graphs and employs an unsupervised graph learning model for pre-training. Then, LGLog mitigates the data imbalance issue by learning significant latent space representation of log events via reconstruction loss and node invariance loss, and further applies a weight balance method. Experiments indicate that LGLog outperforms compared approaches, and demonstrates the effectiveness of LGLog in the presence of scarce labels and imbalanced log data.