Gravity-Inspired Graph Autoencoders for Directed Link Prediction

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.Comment: ACM International Conference on Information and Knowledge Management (CIKM 2019

Visualization of Large Networks Using Recursive Community Detection

Networks show relationships between people or things. For instance, a person has a social network of friends, and websites are connected through a network of hyperlinks. Networks are most commonly represented as graphs, so graph drawing becomes significant for network visualization. An effective graph drawing can quickly reveal connections and patterns within a network that would be difficult to discern without visual aid. But graph drawing becomes a challenge for large networks. Am- biguous edge crossings are inevitable in large networks with numerous nodes and edges, and large graphs often become a complicated tangle of lines. These issues greatly reduce graph readability and makes analyzing complex networks an arduous task. This project aims to address the large network visualization problem by com- bining recursive community detection, node size scaling, layout formation, labeling, edge coloring, and interactivity to make large graphs more readable. Experiments are performed on five known datasets to test the effectiveness of the proposed approach. A survey of the visualization results is conducted to measure the results

FlowMapper.org: A web-based framework for designing origin-destination flow maps

FlowMapper.org is a web-based framework for automated production and design of origin-destination flow maps (https://flowmapper.org). FlowMapper has four major features that contribute to the advancement of existing flow mapping systems. First, users can upload and process their own data to design and share customized flow maps. The ability to save data, cartographic design and map elements in a project file allows users to easily share their data and cartographic design with others. Second, users can customize the flow line symbology by including options to change the flow line style, width, and coloring. FlowMapper includes algorithms for drawing curved line styles with varying thickness along a flow line, which reduces the visual cluttering and overlapping by tapering flow lines at origin and destination points. The ability to customize flow symbology supports different flow map reading tasks such as comparing flow magnitudes and directions and identifying flow and location clusters that are strongly connected with each other. Third, FlowMapper supports supplementary layers such as node symbol, choropleth, and base maps to contextualize flow patterns with location references and characteristics such as net-flow, gross flow, net-flow ratio, or a locational attribute such as population density. FlowMapper also supports user interactions to zoom, filter, and obtain details-on-demand functions to support visual information seeking about nodes, flows and regions. Finally, the web-based architecture of FlowMapper supports server side computational capabilities to process, normalize and summarize large flow data to reveal natural patterns of flows

Graph layout for applications in compiler construction

We address graph visualization from the viewpoint of compiler construction. Most data structures in compilers are large, dense graphs such as annotated control flow graph, syntax trees, dependency graphs. Our main focus is the animation and interactive exploration of these graphs. Fast layout heuristics and powerful browsing methods are needed. We give a survey of layout heuristics for general directed and undirected graphs and present the browsing facilities that help to manage large structured graph

How do persons with brain fatigue after brain injury interact within a social media community? : results from a content and social network analysis

Background: Social media has been shown to be a potentially beneficial tool for the well-being of individuals with chronic medical conditions. However, there is a lack of knowledge on how individuals identified Mild Acquired Cognitive Impairment (MACI) communicate through social media. MACI refers to a non-progressive mild cognitive impairment after an acquired brain injury. Objective: The objectives of this study were to describe the content and to visualize the user involvement in a social media community aimed for people with brain fatigue, a common symptom for persons identified with MACI. Methods: A content- and a social network analysis of the communication of 1092 individuals with brain fatigue, participating in a social media community, were performed. Both quantitative and qualitative methods were used for data analysis. Results: To acknowledge a “like” was the most common form of the studied communicative interactions. Social support (especially informational, but also emotional, and esteem support), and socialization in different forms, were common main themes in the posts and comments. A few individuals were found to be very involved while most of the others were poorly involved in the communication. The involvement followed a long tail distribution. The patterns of produced content themes, and the social media communication features used also varied among the members in the group. Conclusion: This study indicates that a social media group could be a beneficial tool in MACI rehabilitation, because the participants, in varying ways and degrees, socialized and exchanged social support. Such information exchange has been shown to be beneficial in MACI rehabilitation. The results in this study could, in combination with further studies, be analyzed by relevant domain experts in different fields. This could be one step to fill the gap of knowledge on how individuals with MACI are communicating in social media groups.N/

Graph Layout Performance Comparisons of Force-Directed Algorithms

© 2018 Totem Publisher, Inc. All rights reserved. Due to force-directed algorithms’ capabilities of producing aesthetically pleasing graph layouts, which follow metrics for graph drawing aesthetics, these layouts have become the most common methods in the practical data visualization area. However, evaluating the performance of relevant algorithms remains a challenge, since graph layout quality is largely relying on aspects such as human intuition, personal judgment and methods’ pre-setting parameters. In addition, most aesthetics criteria of graph drawing conflict with each other. This study evaluated the performance measurements of four force-directed algorithms in terms of seven commonly applied aesthetic criteria based on practical raw data collected, and demonstrated the experimental framework. The early outcomes compared twenty final graph layouts and gave empirical evidences; the study may assist with future detailed force-directed algorithms selection based on users’ specific requirements