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Traffic forecasting, as the cornerstone of the development of intelligent transportation systems, plays a crucial role in facilitating accurate control and management of urban traffic. By treating sensors as nodes in a road network, recent research on modeling complex spatial-temporal graph structures has achieved notable advancements in traffic forecasting. However, limited by the increasing number of sensors and recorded data points, most of the recent studies on spatial-temporal graph neural network (STGNN) research concentrate on aggregating short-term (e.g. recent one-hour) traffic history to predict future data. Furthermore, almost all previous STGNNs neglect to incorporate the cyclical patterns that appear in the traffic historical data. For example, the cyclical patterns of traffic on the same day or hour of each week can help improve the accuracy of future traffic predictions. In this paper, we propose a novel Dynamic Multi-Granularity Spatial-Temporal Graph Attention Network (DmgSTGAT) framework for traffic forecasting, which leverages multi-granularity spatial-temporal correlations across different time-scales and variables to efficiently consider cyclical patterns in traffic data. We also design effective temporal encoding and transformer encoding layers to produce meaningful multi-granularity sensor-level, day-level, hour-level, and point-level representations. The multi-granularity spatial-temporal graph attention network can use the produced representations to extract useful but sparsely distributed patterns accurately, which also avoids the influence of extra noise from the long-term history. Experimental results on four real-world traffic datasets show that DmgSTGAT can achieve state-of-the-art performance with the help of multi-granularity cyclical patterns compared with various recent baselines. [ABSTRACT FROM AUTHOR]

Exploiting deep learning techniques for traffic flow prediction has become increasingly widespread. Most existing studies combine CNN or GCN with recurrent neural network to extract the spatio-temporal features in traffic networks. The traffic networks can be naturally modeled as graphs which are effective to capture the topology and spatial correlations among road links. The issue is that the traffic network is dynamic due to the continuous changing of the traffic environment. Compared with the static graph, the dynamic graph can better reflect the spatio-temporal features of the traffic network. However, in practical applications, due to the limited accuracy and timeliness of data, it is hard to generate graph structures through frequent statistical data. Therefore, it is necessary to design a method to overcome data defects in traffic flow prediction. In this paper, we propose a long-term traffic flow prediction method based on dynamic graphs. The traffic network is modeled by dynamic traffic flow probability graphs, and graph convolution is performed on the dynamic graphs to learn spatial features, which are then combined with LSTM units to learn temporal features. In particular, we further propose to use graph convolutional policy network based on reinforcement learning to generate dynamic graphs when the dynamic graphs are incomplete due to the data sparsity i sue. By testing our method on city-bike data in New York City, it demonstrates that our model can achieve stable and effective long-term predictions of traffic flow, and can reduce the impact of data defects on prediction results. [ABSTRACT FROM AUTHOR]

Reachability preserving compression generates small graphs that preserve the information only relevant to reachability queries, and the compressed graph can answer any reachability query without decompression. Existing reachability preserving compression algorithms either require a long compression time or include redundant data in the compressed graph. In this paper, we propose a novel edge sorting alogrithm for fast, non-redudant reachability preserving compression. On the other hand, we found that the current incremental reachability compression algorithms for dynamic graphs may return incorrect results in some cases. Therefore, we propose two novel incremental reachability compression algorithms. An algorithm called incremental reachability preserving compression with optimum compression ratio, which generates an update compressed graph that is exactly the same as the graph computed by recompression. The other algorithm called fast incremental reachability preserving compression, which can update the compressed graph in a short time. Extensive experiments on real datasets show the efficiency and the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Betweenness centrality of a vertex (edge) in a graph is a measure for the relative participation of the vertex (edge) in the shortest paths in the graph. Betweenness centrality is widely used in various areas such as biology, transportation, and social networks. In this paper, we study the update problem of betweenness centrality in fully dynamic graphs. The proposed update algorithm substantially reduces the number of shortest paths which should be recomputed when a graph is changed. In addition, we adapt a community detection algorithm using the proposed algorithm to show how much benefit can be obtained from the proposed algorithm in a practical application. Experimental results on real graphs show that the proposed algorithm efficiently update betweenness centrality and detect communities in a graph. [ABSTRACT FROM AUTHOR]