For past GTA3 workshops, please visit our archive page.
In addition, we have been informed by the main conference that the event will take place online due to the COVID-19 pandemic.
Virtual access information will be provided to the registered attendees by IEEE Big Data (via Underline).
Networks are natural analytic tools in modeling adversarial activities (e.g., smuggling, illegal arms dealing, illicit drug production) in different contexts. However, such activities are often covert and embedded across multiple domains. They are generally not detectable and recognizable from the perspective of an isolated network, and only become apparent when multiple networks are analyzed in a joint manner. Thus, one of the main research topics in modeling adversarial activities is to develop effective techniques to align and fuse information from different networks into a unified representation for global analysis. Based on the combined network representation, an equally important research topic is on detecting and matching indicating patterns to recognize the underlining adversarial activities in the integrated network. Furthermore, more sophisticated activities could potentially involve attempts at covering their tracks by attacking and changing networks. Thus, a developing area of interest is focused on attacking the commonly used graph models, which has led towards then using these insights to develop robust models that are resilient to these attacks.
Three key challenge problems involved in the modeling process include:
The focus of this workshop is to gather together the researchers from all relevant fields to share their experience and opinions on addressing the three fundamental graph mining problems – “Connecting the dots”, “Finding a needle in a haystack”, and “Defending against attacks” in the context of adversarial activity analytics.
In addition, this workshop also aims to provide a forum for discussing research challenges and novel approaches in synthesizing realistic networks that those observed in the real-worlds. Numerous approaches have been proposed in the past for generating networks (e.g., exponential random graphs, stochastic Kronecker graphs). However, few research has been conducted on systematically injecting and embedding subtle signals (e.g., covert activities) to these “background” networks. In addition, new methods for generating synthetic data in other domains (e.g., Computer Vision) with deep generative models (e.g., Variational Autoencoders, Generative Adversarial Networks) have grown in prominence. Naturally, the question arises as to whether these new methods can be adapted to the graph domains and how they compare in capability to the current state-of-the-art. Besides the transaction-oriented networks that have been the main focus of this workshop, semantic networks (e.g., knowledge graphs) recently drawn significant attention in our research community. Thus, another important question is to extend the aforementioned graph computing capabilities to handle semantically rich networks to support the emerging research direction.
Including but not limited to:
Other related topics:
This workshop (co-located with the 2020 IEEE International Conference on Big Data) aims to bring together a cross-disciplinary audience of researchers from both academia and industry to share experience techniques, resources and best practices, and to exchange perspectives and future directions. We expect the workshop to develop a community of interested researchers and facilitate their future collaborations. A best paper will be selected and announced in our workshop based on the collective feedback from our reviewers.
Submissions to the workshop will be subject to a single-blind peer review process, with each submission reviewed by at least two program committee members in addition to an organizer. Accepted papers will be given either an oral or poster presentation slot, and will be published in the IEEE Big Data workshop proceedings.
Papers must be submitted in PDF format according to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (Formatting Instructions, Templates) to fit within 10 pages (long papers), or 6 pages (short papers) including any diagrams, references and appendices. Submissions must be self-contained and in English. After uploading your submission, please check the copy stored on the site.
Submissions should be made using the Online Submission System provided by IEEE BigData.
Multi-Channel Entity Alignment via Name Uniqueness Estimation
Miquette Orren, Patrick Mackey, Natalie Heller, and George Chin
Robust and Scalable Entity Alignment in Big Data
James Flamino, Christopher Abriola, Benjamin Zimmerman, Zhongheng Li, and Joel Douglas
Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction
Mariam Alaverdian, William Gilroy, Veronica Kirgios, Xia Li, Carolina Matuk, Daniel Mckenzie, Tachin Ruangkriengsin, Andrea Bertozzi, and Jeffrey Brantingham
Evaluation of Alignment:Precision, Recall, Weighting and Limitations
Joseph Cottam, Natalie Heller, Chrisopher Ebsch, Rahul Deshmukh, Patrick Mackey, and George Chin
Using Graph Edit Distance for Noisy Subgraph Matching of Semantic Property Graphs
Christopher Ebsch, Joseph Cottam, Natalie Heller, Rahul Deshmukh, and George Chin
Data-Driven Template Discovery Using Graph Convolutional Neural Networks
Mikel Joaristi, Sumit Purohit, Rahul Deshmukh, and Geroge Chin
Semantic Guided Filtering Strategy for Best-effort Subgraph Matching in Knowledge Graphs
Alexei Kopylov, Jiejun Xu, Kangyu Ni, Shane Roach, and Tsai-Ching Lu
Fault-Tolerant Subgraph Matching on Aligned Networks
Thomas Tu and Dominic Yang
Static and Dynamic Social Network Models for the Analysis of Transshipment in Illegal Fishing
Stefano Stamato and Andrew Park
Graph Adversarial Attacks and Defense: An Empirical Study on Citation Graph
Chau Pham, Vung Pham, and Tommy Dang
Inexact Attributed Subgraph Matching
Thomas Tu, Jacob Moorman, Dominic Yang, Qinyi Chen, and Andrea Bertozzi
|12:15pm - 12:20pm|||||Opening Remarks|
|12:20pm - 01:00pm|||||Keynote 1: Detecting Anomalous Behavior in Large Graph Databases - Prof. Leman Akoglu|
|01:00pm - 01:40pm|||||Keynote 2: Manifold Structure in Graph Embeddings - Dr. Patrick Rubin-Delanchy|
|01:40pm - 01:55pm|||||Inexact Attributed Subgraph Matching|
|01:55pm - 02:10pm|||||Robust and Scalable Entity Alignment in Big Data|
|02:10pm - 02:20pm|||||Multi-Channel Entity Alignment via Name Uniqueness Estimation|
|02:20pm - 02:30pm|||||Evaluation of Alignment: Precision, Recall, Weighting and Limitations|
|02:30pm - 02:40pm|||||Using Graph Edit Distance for Noisy Subgraph Matching of Semantic Property Graphs|
|02:40pm - 03:00pm|||||Coffee Break|
|03:00pm - 03:40pm|||||Keynote 3: Graph Representation Learning: Recent Advances and Open Challenges - Prof. William Hamilton|
|03:40pm - 04:20pm|||||Keynote 4: Exploring Rare Categories on Graphs: Local vs. Global - Prof. Jingrui He|
|04:20pm - 04:35pm|||||Graph Adversarial Attacks and Defense: An Empirical Study on Citation Graph|
|04:35pm - 04:50pm|||||Data-Driven Template Discovery Using Graph Convolutional Neural Networks|
|04:50pm - 05:00pm|||||Semantic Guided Filtering Strategy for Best-effort Subgraph Matching in Knowledge Graphs|
|05:00pm - 05:10pm|||||Fault-Tolerant Subgraph Matching on Aligned Networks|
|05:10pm - 05:20pm|||||Static and Dynamic Social Network Models for the Analysis of Transshipment in Illegal Fishing|
|05:20pm - 05:30pm|||||Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction|
|05:30pm - 05:35pm|||||Closing Remarks|