The aim of this workshop is to discuss the application of topological techniques to visualizing large networks. Networks are widely used in modeling social, biological and technological systems and to capture relationships among individuals and entities. Understanding their structure is critical to many applications, including neuroscience, epidemiology, law enforcement, public policy and marketing.

This workshop will consist of tutorials and research talks on various topological visualization methods. Specifically, we will explore the development of methods that enable researchers to create and share compelling visualizations of networks for diverse audiences.

In addition, we will examine how the explanatory and persuasive power of visualization can be used to improve our communities. We will also discuss the importance of exposing students to these skills, which are often excluded from standard data analysis curricula.

Topics that will be covered include: Statistical Distributions, Stochastic Algorithms for Feature Extraction and Tracking; In-Situ Data Summary; Relationship Exploration; and Multi-Variable Relationship Visualization.

Statistical Distributions have been extensively studied and utilized in the visualization community for many years as an effective means of efficiently analyzing and visualizing large datasets. Recent developments have shown that statistical distributions can be applied to a wide variety of tasks, including feature identification, extraction and tracking; in-situ data summary; query-driven visualization; multi-variable relationship exploration; and more.

Stochastic Algorithms for the Identification and Tracking of Feature Features

The use of stochastic algorithms in the visualization community has greatly increased in the past few years. These methods enable the efficient analysis of large and complex data sets by identifying characteristic features that appear in specific regions of the data.

These methods have been successful in a number of applications, such as dimensionality reduction [16], shape discovery [1], and computer vision. However, they are still facing some limitations such as time-dependent data, noise and uncertainty, or the need to support emerging data types, such as ensembles or high-dimensional point clouds.

One of the most prominent topological data analysis (TDA) methods is Mapper. Compared to clustering, which focuses on the geometric features of the data points, Mapper is more concerned with their topological properties. In a nutshell, it can distinguish between data points that are uniformly spread over a certain area, such as a disk, from data points that are scattered around a circle or figure-eight shapes.

In addition to being able to detect topological structures, Mapper can also identify clusters of data points with a high concentration of some characteristics. For instance, it can identify clusters of poems composed by two Iranian poets, Ferdowsi and Hafez.

The resulting map can be visualized as a graph with nodes and edges representing data points. The size of the nodes can be regulated to correspond to the number of data points in each cluster and the edge lengths can be based on some chosen characteristics of the data, such as survival rates for breast cancer patients.

Despite their increasing popularity, topological data analysis methods are still facing some limitations such as time-dependent and noisy data, or the need to support emerging data types, e.g., ensemble data or high-dimensional point clouds. This workshop will seek to develop new computational and visual methods that can overcome these challenges by providing a framework for efficient and robust computation and visualizing of subtle structural patterns in multi-scale and time-varying data.

The International Conference on Information is a forum for researchers, engineers and practitioners to share their knowledge in the field of information technology. It is a place where IT professionals stretch across narrowly defined subject areas and acquire a global technical and social perspective.

It is a platform that brings together people from diverse backgrounds and cultures to share their research results, projects, surveys, and industrial experiences in the field of computer science and information technology. It is one of the most important conferences in the world in this field and aims to provide an open forum for academics, students and industry professionals to discuss their latest research developments and achievements.

Authors are invited to submit high quality papers that describe significant advances in the fields of computing and information systems. The papers are to be submitted in the form of full-length (nominal length of 10) original research articles. They are not to be published in any other journal before ICITA 2022. They are to be prepared according to the standard formatting specifications of ICCV and are expected to meet the following criteria:

Paper Matching System

During the submission and review process, the paper is processed by the Toronto system to match it to the best possible area chairs and reviewers. This process is extremely accurate. In addition, papers are sent to reviewers who specialize in the area of the paper and who may have expertise in the specific topic. This is the primary means of ensuring that the paper meets the needs and requirements of the ICCV community.

Confidentiality

The ICCV review process is double blind. This means that the area chairs and reviewers do not know the names of the authors, nor do they know the supplemental material and acknowledgments (i.e., grant IDs, etc.) that the authors provide with their papers. This is the best way to ensure that the quality of the papers is not compromised and that the best papers are given a fair chance for success.

Plagiarism: The ICCV 2023 reviewing process is highly sensitive to plagiarism. It is against the ICCV policy to accept a paper that contains a substantial amount of plagiarized material, regardless of the author’s claim that the material is their own. The ICCV organizers will refer such cases to the IEEE Intellectual Property office. The IEEE has an established mechanism for dealing with plagiarism and wide powers of excluding offending authors from future conferences and IEEE journals.

Special Sessions: ICIST 2022 will also feature special sessions that will be organized by recognized experts within the technical scope of the conference. These special sessions are to be peer-reviewed with the same criteria as the contributed papers.

Attendance responsibilities: The author agrees to participate at the conference and present the paper if it is accepted. In addition, the author must abide by all ICCV 2022 ethics and conference policies.

Papers with Substantial Overlap: If a paper has more than 20 percent overlap with previously or concurrently submitted papers, the authors must inform the ICIST organizers in writing about this. The ICIST organizers will then decide whether to include the paper in the proceedings or not.