CS Events

Qualifying Exam Graph Edge Decompositions for Exploration and Visualization
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Speaker: Haoyang Zhang

Location : CoRE 305

Committee: 

Professor James Abello

Associate Professor Amélie Marian

Assistant Professor Karthik C.. S..

Assistant Professor Roie Levin

Assistant Professor Sumegha Garg  

Event Type: Qualifying Exam

Abstract: A central goal of this research is to provide tools that allow users to obtain humanly-interpretable hierarchical descriptions of any graph data. Thesetools should be accessible via a Unified Web Interface for graph analytics without being constrained by graph data size. By leveraging hierarchical graph edge partitions, our framework allows for interactive visualizations of massive graphs. Our algorithmic tools can be used to create massive data synthesis artifacts that can be fed into machine learning analytics pipelines. These include topological similarity measures for massive dataset "clustering" (e.g., Graph Cities), automatic creation of humanly-understandable summaries of social media posts (e.g., Max Flow Min Cut Views), and disentangling data communities into locally dense subgraphs with interpretable linkages between them (e.g., Community Intersection Graphs). We also illustrate an unsuspected theoretical characterization of a class of graphs derived from a partition of maximal chains in the symmetric group of permutations S_n under the Weak Bruhat Order. These graphs contain the class of visibility graphs of staircase polygons.List of publicationsCentral PapersMassive Graph Exploration[ATZ2024] Abello, J., Tangherlini, T.R., & Zhang, H. (2024). A Max Flow Min Cut View of Social Media Posts. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-707-8; ISSN 2184-285X, SciTePress, pages 190-201. DOI: 10.5220/0012862500003756; Best Paper Award in DATA2024, Dijon, France. [AZNHA2022] Abello, J., Zhang, H., Nakhimovich, D., Han, C., & Aanjaneya, M. (2022). Giga Graph Cities: Their Buckets, Buildings, Waves, and Fragments. IEEE Computer Graphics and Applications, 42(3), 53-64. doi: 10.1109/MCG.2022.3172650; Invited to present at IEEE VIS2023, Melbourne, Australia; Based on BigVis2021 Best Paper Award, Nicosia, Cyprus, 2021.Derived Papers [ABTZ2023] Abello, J., Broadwell, P. M., Tangherlini, T. R., & Zhang, H. (2023). Disentangling the Folklore Hairball: A Network Approach to the Characterization of a Large Folktale Corpus. Fabula, 64(1-2), 64-91. DOI: 10.1515/fabula-2023-0004[AZ2023] Abello, J., & Zhang, H. (2023). Graph Peeling Semantics. BigVis2023, CEUR Workshop Proceedings, 3379. URL: CEUR-WS.org/Vol-3379/BigVis2023_705.pdf

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Contact  Professor James Abello

Zoom link: https://rutgers.zoom.us/j/96994166182?pwd=zjGWDw0ZmRVJawG6i3B1b5LG5q5fOD.1