In this paper we use a time-evolving graph which consists of a sequence of graph snapshots over time to model many real-world networks. We study the path classification problem in a time-evolving graph, which has many applications in real-world scenarios, for example, predicting path failure in a telecommunication network and predicting path congestion in a traffic network in the near future. In order to capture the temporal dependency and graph structure dynamics, we design a novel deep neural network named Long Short-Term Memory R-GCN (LRGCN). LRGCN considers temporal dependency between time-adjacent graph snapshots as a special relation with memory, and uses relational GCN to jointly process both intra-time and inter-time relations. We also propose a new path representation method named self-attentive path embedding (SAPE), to embed paths of arbitrary length into fixed-length vectors. Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.

No abstract

No abstract

In this study, the occurrence and distribution of 16 polycyclic aromatic hydrocarbons (PAHs), listed by the United States Environmental Protection Agency (US EPA), were investigated in surface sediment samples from the Hun River, northeast China. The data was then used to assess the potential ecological risk. [g,h,i]perylene) showed that they had been emitted from a number of different sources, especially the pyrolytic emissions. The results of the ecological risk assessment, which compared the PAH concentrations with the effect range low (ERL) and the effect range median (ERM) values, indicated that several individual PAH concentrations at four sites in the downstream section of the Hun River were higher than the ERM, suggesting that there was a potential ecological risk in these areas.