A Survey on Embedding Dynamic Graphs

Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output. We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area.

Virtual-Reality graph visualization based on Fruchterman-Reingold using Unity and SteamVR

The paper describes a method for the immersive, dynamic visualization of undirected, weighted graphs. Using the Fruchterman-Reingold method, force-directed graphs are drawn in a Virtual-Reality system. The user can walk through the data, as well as move vertices using controllers, while the network display rearranges in realtime according to Newtonian physics. In addition to the physics behind the employed method, the paper explains the most pertinent computational mechanisms for its implementation, using Unity, SteamVR, and a Virtual-Reality system such as HTC Vive (the source package is made available for download). It was found that the method allows for intuitive exploration of graphs with on the order of [Formula: see text] vertices, and that dynamic extrusion of vertices and realtime readjustment of the network structure allows for developing an intuitive understanding of the relationship of a vertex to the remainder of the network. Based on this observation, possible future developments are suggested.

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.

A term-based and citation network-based search system for COVID-19

The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. The system is based on automatically identified technical terms, document citations, and their visualization, accelerating identification of relevant documents. It offers a multi-view interactive search and navigation interface, bringing together unsupervised approaches of term extraction and citation analysis. We conducted a user evaluation with domain experts, including epidemiologists, biochemists, medicinal chemists, and medicine students. In general, most users were satisfied with the relevance and speed of the search results. More interestingly, participants mostly agreed on the capacity of the system to enable exploration and discovery of the search space using the graph visualization and filters. The system is updated on a weekly basis and it is publicly available at http://www.nactem.ac.uk/cord/.

“WHAT IS OUV” REVISITED: A COMPUTATIONAL INTERPRETATION ON THE STATEMENTS OF OUTSTANDING UNIVERSAL VALUE

The Statements of Outstanding Universal Value (OUV) concerns the core justification for nominating and inscribing cultural and natural heritage properties on the UNESCO World Heritage List, ever since 2007. Ten criteria are specified and measured independently for the selection process. The 2008 ICOMOS Report “What is OUV” has been a successful example to interpret OUV as an integral concept by inspecting the associations of the selection criteria in all inscribed properties. This paper presents a novel methodology for interpreting OUV using computational techniques of Natural Language Processing, Machine Learning, and Graph Visualization. Firstly, frequent phrases appearing in Statements of OUV are used to construct a lexicon for each selection criterion; Secondly, three similarity matrices are constructed as graphs to represent the pair-wise associations of the criteria; Lastly, the lexicon and graphs are visualized in 2D. The study shows that the lexicon derived from computational techniques can capture the essential concepts of OUV, and that the selection criteria are consistently associated with each other in different similarity metrics. This study provides a quantitative and qualitative interpretation of the Statements of OUV and the associations of selection criteria, which can be seen as an elaborated computational extension of the 2008 Report, useful for future inscription and evaluation process of World Heritage nominations.

C-Planarity Testing of Embedded Clustered Graphs with Bounded Dual Carving-Width

For a clustered graph, i.e, a graph whose vertex set is recursively partitioned into clusters, the C-Planarity Testing problem asks whether it is possible to find a planar embedding of the graph and a representation of each cluster as a region homeomorphic to a closed disk such that (1) the subgraph induced by each cluster is drawn in the interior of the corresponding disk, (2) each edge intersects any disk at most once, and (3) the nesting between clusters is reflected by the representation, i.e., child clusters are properly contained in their parent cluster. The computational complexity of this problem, whose study has been central to the theory of graph visualization since its introduction in 1995 [Feng, Cohen, and Eades, Planarity for clustered graphs, ESA’95], has only been recently settled [Fulek and Tóth, Atomic Embeddability, Clustered Planarity, and Thickenability, to appear at SODA’20]. Before such a breakthrough, the complexity question was still unsolved even when the graph has a prescribed planar embedding, i.e, for embedded clustered graphs. We show that the C-Planarity Testing problem admits a single-exponential single-parameter FPT (resp., XP) algorithm for embedded flat (resp., non-flat) clustered graphs, when parameterized by the carving-width of the dual graph of the input. These are the first FPT and XP algorithms for this long-standing open problem with respect to a single notable graph-width parameter. Moreover, the polynomial dependency of our FPT algorithm is smaller than the one of the algorithm by Fulek and Tóth. In particular, our algorithm runs in quadratic time for flat instances of bounded treewidth and bounded face size. To further strengthen the relevance of this result, we show that an algorithm with running time O(r(n)) for flat instances whose underlying graph has pathwidth 1 would result in an algorithm with running time O(r(n)) for flat instances and with running time $$O(r(n^2) + n^2)$$ O ( r ( n 2 ) + n 2 ) for general, possibly non-flat, instances.

The program of the advanced training course for teachers of mathematics and informatics "Graphs and graph models: methods of visual processing "

The method of visual presentation of educational information for solving problems of mathematics and informatics is effective for the development of algorithmic, logical and computational thinking of schoolchildren. Technical progress, informatization of education, the emergence of modern software for visualization of information change the activities of teachers who need to master new technologies of information visualization for use in the classroom and in work with gifted children. Visual models for presenting educational information and methods of their processing with the use of computer programs are also relevant in extracurricular activities, allowing to develop the intellectual abilities of schoolchildren. Teachers are required to teach children to create projects in which visibility is a necessary component and must be represented by an electronic product created using modern information visualization tools. The article proposes a variant of the advanced training course for teachers of mathematics and informatics on teaching methods for visualization of solving problems using graphs and the free software “Graphoanalyzator”. The relevance of the course is due to the need to form the competency to carry out targeted work with gifted children in the use of software for creating and processing graphs based on the graph visualization program “Graphoanalyzator”. The authors believe that the training of teachers on this course will contribute to the formation of their skills to solve problems of mathematical modeling in informatics and mathematics, to apply information technologies to solve pedagogical problems in the context of informatization of education.

A Geo-Dashboard Concept for the Interactively Linked Visualization of Provenance and Data Quality for Geospatial Datasets

In Earth System Sciences, a data-driven research domain, several communities discuss the importance, guidance and implementation of making research data findable, accessible, interoperable, and reusable. To foster these principles, in particular to support reusability, users need easy-to-use user interfaces with meaningful visualizations for detailed metainformation, e.g. on dataset’s origin and quality. However, visualization tools to facilitate the evaluation of fitness for use of ESS research data on domainspecific metainformation, do hardly exist.We provide a Geo-dashboard concept for user-friendly interactive and linked visualizations of provenance and quality information using standardized geospatial metadata. A provenance graph visualization serves as overview and entry point for further evaluations. Quality information is essential to evaluate the fitness for use of data. Therefore, we developed quality visualizations on several levels of detail to foster evaluation, e.g. by enabling users to choose and classify quality parameters based on their use-case-specific needs.