# Multi-NETVIS: Visual Analytics for Multivariate Network
###### Abstract  
In the process of multivariate network exploration and analysis, it is important to consider network topology and attribute correlation analysis. In order to consider both in presentation and analysis, existing research focuses on visual design and multiple-view visualization. However, this multititonous visual design makes network cognition and analysis difficult. In multi-view visualization, the associated information among attributes is rarely retained and is often accompanied by tedious interaction processes. In this paper, a layout scheme is proposed to balance attribute and topology analysis in multivariate network visual analysis and a multivariate network visual analytics system is implemented based on the layout scheme. The analysis scenarios of overall, community, and local multi-granularity are provided by the layout scheme, which combines 3D, 2.5D, and 2D layouts. According to the layout scheme, we propose a layout transformation method to maintain the relative position of the topological context layouts in three dimensions. Furthermore, we propose a Louvain-3D FDA layout algorithm for the 3D layout, and introduce an edge bundling algorithm in the 2.5D layout to achieve an attribute-oriented topology layout. Combining the principle of interaction from global to detail, we design a novel system, Multi-NetVis, which supports users in drilling exploration and analysis and takes both the network attribute correlations and topological structure into consideration. Finally, two datasets are selected to demonstrate the usage scenarios and an evaluation experiment is designed to verify the effectiveness of the layout scheme. network visual analytics; multivariate network; multi-dimensional spatial transformation; community detection 2022128405 20221  
for example, in the Weibo corpus related to Ebola during the 2014 Ebola outbreak in West Africa [2]. In addition, several studies have developed general network analysis tools, such as Gephi, Cytoscape, etc. Nevertheless, these comprehensive network analysis tools lack guidance and require a professional knowledge background and much time to find breakthroughs. For multivariate network visual analysis, attributes and topological structures can be presented through diversified visual expressions with multi-view collaborative interactions. However, these methods are limited by their single-dimensional spatial layouts. Presenting multiple attributes simultaneously in the topology structure increases visual complexity; selectively presenting certain attributes is inconvenient for comparison and association analysis among the attributes. In addition, the association analysis between topology structures and attributes cannot be flexibly supported; for example, topology structures with multiple attributes cannot be compared. Aiming at the limitations of visual analytics of multivariate networks in single-dimensional spatial layouts, a multi-dimensional integrated layout scheme is proposed in this paper. A layout scheme is proposed in which the global network structure is presented in 3D space, which is effective in allowing users to master the global topology of the network. We note that community exploration is efficient both for quickly understanding network topology and for mining potential correlations [3; 4]. Therefore, the Louvain community detection algorithm [5] is introduced to conduct a preliminary analysis of the network, and in combination with the force-directed algorithm we achieve a Louvain-3D FDA algorithm. In addition, the 2.5D hierarchical layout is drawn on multiple 2D planes and arranged in the third dimension, with the layers indicating the network attributes. To reduce visual confusion caused by crossing edges, an edge bundling algorithm is used to optimize edge crossing among multiple layers. A 2D network layout view design is used to present details of local networks on a 2D surface. Additional analysis perspectives and richer network information are presented according to the provided multiple dimensional spatial layouts scheme.