使用嵌入引导布局的图形探索
Graph Exploration with Embedding-Guided Layouts
摘要：
节点链接图被广泛用于可视化图形。大多数图形布局算法仅使用图形拓扑用于美学目标（例如，最小化节点遮挡和边缘交叉）或使用节点属性用于探索目标（例如，可见的社区）。结合两个视角的现有混合方法仍然受到各种生成限制（例如，有限的输入类型和需要的手动调整和图形的先验知识）以及美学和探索目标之间的不平衡。在本文中，我们提出了一个灵活的基于嵌入的图形探索管道，享受最好的图形拓扑结构和节点属性。首先，我们利用属性图的嵌入算法将两个视角编码到潜在空间中。然后，我们提出了一个嵌入驱动的图布局算法，GEGbomb，它可以实现美学布局与更好的社区保护，以支持一个简单的解释的图结构。接下来，基于生成的图布局和从嵌入向量提取的见解来扩展图探索。结合实例，提出了一种基于Focus+Context交互的保持布局的聚合方法和一种基于多种邻近度策略的相关节点搜索方法。最后，我们进行定量和定性评估，用户研究，和两个案例研究，以验证我们的方法。
英文摘要：
Node-link diagrams are widely used to visualize graphs. Most graph layout algorithms only use graph topology for aesthetic goals (e.g., minimize node occlusions and edge crossings) or use node attributes for exploration goals (e.g., preserve visible communities). Existing hybrid methods that bind the two perspectives still suffer from various generation restrictions (e.g., limited input types and required manual adjustments and prior knowledge of graphs) and the imbalance between aesthetic and exploration goals. In this paper, we propose a flexible embedding-based graph exploration pipeline to enjoy the best of both graph topology and node attributes. First, we leverage embedding algorithms for attributed graphs to encode the two perspectives into latent space. Then, we present an embedding-driven graph layout algorithm, GEGraph, which can achieve aesthetic layouts with better community preservation to support an easy interpretation of the graph structure. Next, graph explorations are extended based on the generated graph layout and insights extracted from the embedding vectors. Illustrated with examples, we build a layout-preserving aggregation method with Focus+Context interaction and a related nodes searching approach with multiple proximity strategies. Finally, we conduct quantitative and qualitative evaluations, a user study, and two case studies to validate our approach.
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