Traffic forecasting is a core element of intelligent traffic monitoring system. Approaches based on graph neural networks have been widely used in this task to effectively capture spatial and temporal dependencies of road networks. However, these approaches can not effectively define the complicated network topology. Besides, their cascade network structures have limitations in transmitting distinct features in the time and space dimensions. In this paper, we propose a Multi-adaptive Spatiotemporal-flow Graph Neural Network (MAF-GNN) for traffic speed forecasting. MAF-GNN introduces an effective Multi-adaptive Adjacency Matrices Mechanism to capture multiple latent spatial dependencies between traffic nodes. Additionally, we propose Spatiotemporal-flow Modules aiming to further enhance feature propagation in both time and space dimensions. MAF-GNN achieves better performance than other models on two real-world datasets of public traffic network, METR-LA and PeMS-Bay, demonstrating the effectiveness of the proposed approach.
Graph layout investigates the structure of the graph in order to better obtain the information implied in the graph. To solve the shortcomings of dimension reduction layouts on local adjustment and the insufficiency of energy models to maintain the overall structure of the graphs, this paper proposes a new graph layout framework called ''tNEM'' that layouts graphs by combining t-distributed neighbor retrieval visualizer (t-NeRV) and energy models. In the process of layout, our algorithm considers global and local structures at the same time. The layout results are more conform to aesthetic standards, meanwhile, maintain the structural information of the graph. We evaluate our algorithm on a wide variety of datasets and compare it with many other methods. We produce better visualization results than tsNET and tsNET* methods by reducing the tendency to crowd points together, and can better capture the global structure of the graph.
Exploring large graphs is difficult due to their large size and semantic information such as node attributes. Extracting only a subgraph relevant to the user-specified nodes (called focus nodes) is an effective strategy for exploring a large graph. However, existing approaches following this strategy mainly focus on graph topology and do not fully consider node attributes, resulting in the lack of clear semantics in the extracted subgraphs. In this paper, we propose a novel approach called TS-Extractor that can extract a relevant subgraph around the user-selected focus nodes to help the user explore the large graph from a local perspective. By combining the graph topology and the user-selected node attributes, TS-Extractor can extract and visualize a connected subgraph that contains as many nodes sharing the same/similar attribute values with the focus nodes as possible, thereby providing the user with clear semantics. Based on TS-Extractor, we develop a Web-based graph exploration system that allows users to interactively extract, analyze and expand subgraphs. Through two case studies and a user study, we demonstrate the usability and effectiveness of TS-Extractor.
Ego-network, which can describe relationships between a focus node (i.e., ego) and its neighbor nodes (i.e., alters), often changes over time. Exploring dynamic ego-networks can help users gain insight into how each ego interacts with and is influenced by the outside world. However, most of the existing methods do not fully consider the multilevel analysis of dynamic ego-networks, resulting in some evolution information at different granularities being ignored. In this paper, we present an interactive visualization system called DyEgoVis which allows users to explore the evolutions of dynamic ego-networks at global, local and individual levels. At the global level, DyEgoVis reduces dynamic ego-networks and their snapshots to 2D points to reveal global patterns such as clusters and outliers. At the local level, DyEgoVis projects all snapshots of the selected dynamic ego-networks onto a 2D space to identify similar or abnormal states. At the individual level, DyEgoVis utilizes a novel layout method to visualize the selected dynamic ego-network so that users can track, compare and analyze changes in the relationships between the ego and alters. Through two case studies on real datasets, we demonstrate the usability and effectiveness of DyEgoVis.
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