Drawing and Visualisation of Dynamic Multivariate Graphs for Social Networks

网络，或图形，在我们的现代社会中无处不在。网络由实体或节点以及这些实体或边之间的一组关系组成。当我们考虑社交媒体服务（Facebook，Twitter等）时，节点对应于用户帐户，边缘是这些用户之间的友谊。节点通常具有与它们相关联的信息或属性。在社交网络中，这些属性可以像人口统计数据或特定用户在给定时间拥有的信息一样简单。在智能基础设施设置中，节点和边缘对应于道路网络，道路沿着的拥堵程度将是网络的一个属性。一般来说，具有与其节点和边相关联的属性的网络被称为多变量网络，并且是信息可视化的新兴领域。多变量网络也可以是动态的。也就是说，网络本身的结构或与节点和边相关联的属性可以随时间而改变。在社交网络环境中，友谊的形成和破裂，使网络不断发展。朋友可能拥有的信息可以通过网络结构从一个节点传播到另一个节点。在一个城市中，新的道路可以被建造或关闭，这与网络中的结构变化相对应。拥堵程度在一天中会有所不同，因此属性也会发生变化。结构和/或属性随时间变化的网络是动态的多变量网络，理解它们如何运行对于这些应用和许多其他应用至关重要。在信息可视化中，这一新兴领域最近受到了广泛关注。这项工作的挑战是当网络结构和/或属性随时间变化时，我们希望有效地可视化这些变化。由于许多网络，特别是社交网络，没有节点的固有空间位置，我们可以自由选择这些位置。此外，我们可以自由选择属性的编码。然而，我们必须以一种对可视化的最终用户既有计算效率又有感知效率的方式来做到这一点。在这项工作中，我们着眼于感知有效的方法来绘制和可视化动态多变量图。我们与牛津互联网研究所的研究科学家密切合作，以确保对各种用户社区已经使用的软件的影响。对于这项工作，我们受到心理学和感知研究的启发，以推动算法的设计，绘制图形和交互式方法，以实现可视化。我们专注于绘制和可视化演化结构和演化属性的方法。为了确保我们的算法符合预期的感知标准，我们通过度量评估来评估我们的计算过程，并通过以用户为中心的实验来评估我们的可视化。通过与社交网络的合作者密切合作，我们确保工作与分析社交网络相关。

Networks, or graphs, are pervasive in our modern society. A network consists of entities, or nodes, and a set of relationships between those entities, or edges. When we consider social media services (Facebook, Twitter, etc), nodes correspond to user accounts and edges would be friendships between these users. Nodes often have information associated with them, or attributes. In a social network, these attributes could be as simple as demographic data or information that a particular user has at a given time. In an intelligent infrastructure setting, where nodes and edges correspond to a road network, the level of congestion along stretches of road would be an attribute of the network. In general, networks that have attributes associated with their nodes and edges are called multivariate networks and are an emerging area of information visualisation.Multivariate networks can also be dynamic. That is, either the structure of the network itself or the attributes associated with the nodes and edges can change over time. In a social network setting, friendships are formed and are broken, evolving the network. Information that a friend might have can spread from node to node cascading through the network structure. In a city, new roadways can be built or closed, corresponding to structural changes in the network. Levels of congestion vary throughout the course of the day and therefore the attributes can change as well. Networks where the structure and/or attributes change over time are dynamic multivariate networks and understanding how they operate is critical for these applications and many others. In information visualisation, this emerging area has received much recent attention.The challenge of this work occurs when the network structure and/or the attributes change over time and we would like to visualise these changes effectively. As many networks, particularly social networks, do not have inherent spatial positions for the nodes, we are free to choose these positions. Also, we are free to choose the encodings for the attributes. However, we must do so in a way that is both computationally efficient and perceptually effective for the end user of the visualisation. In this work, we look at perceptually effective ways for drawing and visualising dynamic multivariate graphs. We work closely with research scientists at the Oxford Internet Institute to ensure impact on software that is already in use with various user communities. For this work, we are inspired by studies in psychology and perception in order to drive the design of algorithms to draw the graph and interactive methods for their visualisation. We concentrate on methods for drawing and visualising both evolving structure and evolving attributes. In order to ensure our algorithms conform to the desired perceptual criteria, we evaluate our computational processes through metric evaluations and evaluate our visualisations through user centred experimentation. By working closely with our collaborators in social networks, we ensure the work is relevant for analysing social networks.