Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories

TL;DR

DyGETViz is a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems and establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines.

Abstract

We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent advancements in discrete-time dynamic graph (DTDG) models to adeptly handle the temporal dynamics inherent in dynamic graphs. DyGETViz effectively captures both micro- and macro-level structural shifts within these graphs, offering a robust method for representing complex and massive dynamic graphs. The application of DyGETViz extends to a diverse array of domains, including ethology, epidemiology, finance, genetics, linguistics, communication studies, social studies, and international relations. Through its implementation, DyGETViz has revealed or confirmed various critical insights. These include the diversity of content sharing patterns and the degree of specialization within online communities, the chronological evolution of lexicons across decades, and the distinct trajectories exhibited by aging-related and non-related genes. Importantly, DyGETViz enhances the accessibility of scientific findings to non-domain experts by simplifying the complexities of dynamic graphs. Our framework is released as an open-source Python package for use across diverse disciplines. Our work not only addresses the ongoing challenges in visualizing and analyzing DTDG models but also establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines.

Figures (11)

• Figure 1: Visualization of Reddit online communities. Each gray node in the background represents an online community ("subreddit"). The trajectories of five groups of subreddits are displayed, including a. gaming, b. sports, c. video-sharing, d. politics, and e. music. Text in the background indicates the topics that characterize each subreddit cluster. Different video-sharing communities (c.) manifest diverse levels of specialization, where communities with a narrow focus of video promotion demonstrate less mobility than general-purpose communities. DyGETViz captures a major event in r/The_Donald -- its shutdown.
• Figure 2: a. Chronological evolution of Chinese lexicon pertaining to environmental protection. These words exhibit diverse meanings from the 1950s onward, culminating in a cohesive cluster by the 1990s. This trend underscores the growing prominence and consolidation of environmental protection concepts within the analyzed corpus. English translations are provided for reference. b. Semantic Shift in LGBTQ+ Terminology. The word "gay" was initially synonymous with "joy" and "happiness," but its usage progressively aligns with homosexuality. This shift underscores the changing societal discourse and recognition of LGBTQ+ identities. c. Comparative analysis of semantic stability using RBO and $\textrm{Jaccard}_{100}$ reveal that words related to homosexuality exhibit substantial shifts in meaning since their inception, reflecting societal changes in perception and language. In contrast, terms solely associated with happiness show remarkable semantic stability, highlighting the enduring nature of certain lexicons despite evolving societal contexts.
• Figure 3: a. Overview of the embedding trajectories on UN Comtrade comtrade2010united. Each country is labeled with its nominal GDP rankings in 2017 GDP2017 (e.g.,"USA (1)"). Large- and middle-scale economies (e.g., USA, UK, Russia, Netherlands) with higher GDP rankings and intensive trade relations form a distinct cluster, while lower-ranked economies (e.g., Tajikistan, Uzbekistan, Jordan) exhibit individual clusters; b. Detailed view of a. The three country groups according to IMF IMFCountryGroups, Major Advanced Economies (MAE), Other Advanced Economies (OAE), and Emerging and Developing Economies (EDE), form distinct visual partitions. The trajectories of advanced economies with economic stability, such as the USA, UK, and Germany, remain in a constrained region, while countries that have experienced rapid growth or drastic economic instability, such as Japan, China, and Russia, manifest more diverse trajectories; c. Fluctuations in $\operatorname{Jaccard}_n$ and RBO align with major economic events in history. d. Average RBO of each country over the period 1988-2022. The x-axis describes the total GDP on a logarithmic scale. Node colors indicate country types, and node sizes represent the population.
• Figure 4: a/d. t-SNE visualization of the Aging dataset li2021improved and the DGraph dataset huang2022dgraph. Red dots represent aging-related genes in the genetic network (a) and fraudsters in the financial network (d). Gray dots represent normal nodes (non-aging-related genes and normal users), respectively. b/e. embedding trajectories of 10 anomalous nodes (in warm colors) and 10 normal nodes (in cold colors), respectively c/f. The kernel density estimate (KDE) plot for the trajectories. Darker colors indicate higher node densities.
• Figure S1: Our proposed DyGETViz framework.