Open-Set Graph Anomaly Detection via Normal Structure Regularisation

This paper considers an under-explored Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to detect anomalous nodes using a small number of labelled training normal and anomaly nodes (known as seen anomalies) that cannot illustrate all possible inference-time abnormalities. The task has attracted growing attention due to the availability of anomaly prior knowledge from the label information that can help to substantially reduce detection errors. However, current methods tend to over-emphasise fitting the seen anomalies, leading to a weak generalisation ability to detect unseen anomalies, i.e., those that are not illustrated by the labelled anomaly nodes. Further, they were introduced to handle Euclidean data, failing to effectively capture important non-Euclidean features for GAD. In this work, we propose a novel open-set GAD approach, namely normal structure regularisation (NSReg), to leverage the rich normal graph structure embedded in the labelled nodes to tackle the aforementioned two issues. In particular, NSReg trains an anomaly-discriminative supervised graph anomaly detector, with a plug-and-play regularisation term to enforce compact, semantically-rich representations of normal nodes. To this end, the regularisation is designed to differentiate various types of normal nodes, including labelled normal nodes that are connected in their local neighbourhood, and those that are not connected. By doing so, it helps incorporate strong normality into the supervised anomaly detector learning, mitigating their overfitting to the seen anomalies. Extensive empirical results on real-world datasets demonstrate the superiority of our proposed NSReg for open-set GAD

A SiO J = 5 - 4 Survey Toward Massive Star Formation Regions

We performed a survey in the SiO $J=5\rightarrow4$ line toward a sample of 199 Galactic massive star-forming regions at different evolutionary stages with the SMT 10 m and CSO 10.4 m telescopes. The sample consists of 44 infrared dark clouds (IRDCs), 86 protostellar candidates, and 69 young \HII\ regions. We detected SiO $J=5\rightarrow4$ line emission in 102 sources, with a detection rate of 57\%, 37\%, and 65\% for IRDCs, protostellar candidates, and young \HII\ regions, respectively. We find both broad line with Full Widths at Zero Power (FWZP) $>$ 20 \kms and narrow line emissons of SiO in objects at various evolutionary stages, likely associated with high-velocity shocks and low-velocity shocks, respectively. The SiO luminosities do not show apparent differences among various evolutionary stages in our sample. We find no correlation between the SiO abundance and the luminosity-to-mass ratio, indicating that the SiO abundance does not vary significantly in regions at different evolutionary stages of star formation.Comment: 25 pages, 9 figures, 5 tables, accepted for publication in Ap

An Application of Hierarchical Structure Model for Trip Mode Choice Forecasting in China

Trip mode split is the result of interrelated and mutually independent factors, such as city scale, urban form, economic level, trip distance, and travel time. In order to analyze the formation of traffic structure, it is necessary to make a comprehensive study on the mechanism of these factors and obtain the basic causal relationship of them. Based on this, by using the hierarchical structure model in system engineering, this paper firstly clarifies the logical relationship of different factors. Then, the existing trip survey data of several cities is used to establish the mathematical relationship of various factors of the structure model. Finally, the mode choice forecasting method is proposed based on the structure model of influencing factors. The case study result of six cities shows small bias, indicating that the proposed method is of great practical value. Policy makers can use the results to discover the trip structure feature and grasp the direction of transportation development policy

Modeling Pedestrian’s Conformity Violation Behavior: A Complex Network Based Approach

Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network’s degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian’s illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian’s conformity violation behavior will increase as the spreading rate increases