Analysis and Visualization of Spatial-Social Networks

Computer Science Department

Kent State University

Project Introduction

In this project, we tackle a useful problem, Querying Over Spatial Social Networks, that returns communities (i.e., groups of users) with strong influence factors such as keywords, relationships, and POIs satisfying some constraints. Users are combined into clusters based on their degree of similarity with other users. The degree of similarity is then calculated with all other clusters. We propose efficient and effective algorithms to enable fast user clustering, indexing, and querying of users and design a user-friendly graphical user interface (GUI) to interact with our developed prototype system. Specific attention will be placed on enhancing the efficiency of the community search query.

Contributions

                    Formalized the problem of community search over spatial-social networks.

                    Provide a user-friendly interface to plot, visualize, and analyze spatial-social network.

                    Design efficient techniques for community searching.

Documentation

COF 2021 Presentation: Efficient and Effective Management and Analytics Over Spatial-Social Networks

URLs: http://www.cs.kent.edu/~xlian/projects/COF2022_CS_SSN/Efficient and Effective Management and Analytics Over Spatial-Social Networks.pptx

https://docs.google.com/presentation/d/1_H94K5i0Hx6vJhQ__pi_R6ELPO8fiaVeH6fShm1R1nI/edit?usp=sharing

 

COF 2022 Poster Presentation: COF2022_Poster.pdf

URL: http://www.cs.kent.edu/~xlian/projects/COF2022_CS_SSN/COF2022_Poster.pdf

https://drive.google.com/file/d/1gf2cTvYFxLJMmSPWHIXGfnkc33O4_DHR/view?usp=sharing

 

COF 2023 Poster Presentation:

URL: https://drive.google.com/file/d/1EGClppj0jEmS718Y_hgTz11xyF5ictWC/view?usp=sharing

 

COF 2024 Presentation:

URL: https://docs.google.com/presentation/d/1SuR1I8o8_sJt6PbAIPYwP9HxCaSGY-k8FQNDfqnYO_A/edit?usp=sharing

 

 

GitHub Code Repository: https://github.com/InfernoX5515/Spatial-SocialNetworks

 


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Awards

2021 COF Poster Symposium

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2023 COF Poster Symposium – Award of Excellence for Outstanding Presentation

 

2024 COF Poster Symposium – Award of Excellence for Outstanding Presentation

Demo

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When the application is first loaded, the user is shown a summary of the loaded road and social network datasets.

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The user can switch views and execute a cluster query on the data, with real-world clusters being displayed on the right with interactive, draggable clusters appearing on the right.

 

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The user can also execute a community search query on the query using the toolbar at the top of the application. Users who satisfy the query are displayed as interactive nodes on the left. User's physical locations are displayed on the right, with the query user being a green star. Users who satisfy the query conditions are plotted as blue nodes. Users who do not satisfy the conditions but are needed to connect users are plotted as grey nodes

 

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The user can also execute a temporal community search. This is identical to the above community search, except the user can specify a date range for users in the community. A timeline on the bottom of the screen allows the user to step through the query to see the community formed with time. The first and last frame results from the entire provided time interval.

 

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The timeline on the bottom of the screen allows the time query to be stepped through. The community gets bigger with each frame on the timeline. The timeline can play automatically or be stepped through by the users. Dark blue nodes are new users to the community, and light blue nodes were added to the community in a previous time interval.

 

 

 

Future Work

Our current focus is on optimizing our application.

·         Performance could still be better optimized.

·         Better caching of datasets for faster loading.

·         Implement more precomputation to save time for querying.

·         Add the ability to export query results for later analysis.

·         Allow for more interaction.

Team Members

Halie Eckert

Bachelor of Science in Computer Science class of 2024

Email: HEckert1@kent.edu

Gavin Hulvey

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Bachelor of Science in Computer Science class of 2024

Email: GHulvey@kent.edu

 

Nathan Wolfe

Bachelor of Science in Computer Science class of 2025

Email: NWolfe8@kent.edu

Sydney Zuelzke

Bachelor of Science in Computer Science class of 2025

Email: SZuelzke@kent.edu

Xiang Lian (Advisor)

Email: xlian@kent.edu

Homepage: http://www.cs.kent.edu/~xlian/index.html

 

 

Past Members

o   Andrew Hughes

o   Lorenzo Bair

 

 

Last Modified: 4/10/2024