What is CANON

How does it work?

CANON centers on a network-of-networks (NoN) model, with nodes and edges defined across different domains at multiple resolutions. We address the key challenges of modeling adversarial activities via four technical components:

• Optimization-based network alignment
• Network embedding and conditioning
• Approximate subgraph matching
• Investigative subgraph discovery

The first component aims to identify node correspondence across networks. Our system augments traditional topological-based techniques by aligning networks with rich attribute information. This allows for more accurate node mapping, especially in cases where input networks are noisy, incomplete, or ambiguous. The second component enables simultaneous projection of nodes or subgraphs from multiple networks into a common embedding space.

This allows for effective matching and comparisons between data entities. The third component enables fast and approximate matching of activity templates in the NoN, which correspond to complex adversarial activity patterns in the real world. The fourth component aims to discover atypical and anomalous subgraph regions in the absence of specific activity templates.

Publications

	Characterizing Disease Spreading via Visibility Graph Embedding IEEE BigData, GTA3 Workshop 2021 | K. Ni, J. Xu, S. Roach, T. Lu, and A. Kopylov
	DESTINE: Dense Subgraph Detection on Multi-Layered Networks ACM CIKM, 2021 | Z. Xu, S. Zhang, Y Xia, L. Xiong, J. Xu, and H. Tong
	KompaRe: A Knowledge Graph Comparative Reasoning System ACM KDD, 2021 | L. Liu, B. Du, Y. Fung, H. Ji, J. Xu, and H. Tong
	Neural-Answering Logical Queries on Knowledge Graphs ACM KDD 2021 | L. Liu, B. Du, H. Ji, C. Zhai, and H. Tong
	Semantic Guided Filtering Strategy for Best-effort Subgraph Matching in Knowledge Graphs IEEE BigData, GTA3 workshop 2020 | A. Kopylov, J. Xu, C. Ni, S. Roach, and T. Lu
	CANON: Complex Analytics of Network of Networks IEEE BigData, 2020 | S. Roach, C. Ni, A. Kopylov, T. Lu, J. Xu, S. Zhang, B. Du, D. Zhou, J. Wu, L. Liu, Y. Yan, J. He, and H. Tong
	Local Motif Clustering on Time-Evolving Graphs [Code] ACM KDD 2020 | D. Fu, D. Zhou, J. He
	NetTrans: Neural Cross-Network Transformation ACM KDD 2020 | S. Zhang, H. Tong, Y. Xia, L. Xiong, and J. Xu
	Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting [Code] ACM WWW 2020 | D. Zhou, L. Zheng, Y. Zhu, J. Li, and J. He
	Incomplete Network Alignment: Problem Definitions and Fast Solutions ACM TKDD 2020 | S. Zhang, H. Tong, J. Tang, J. Xu, W. Fan
	Graph Convolutional Networks: A Comprehensive Review Springer Computational Social Networks 2019 | S. Zhang, H. Tong, J. Xu and R. Maciejewski
	Filtering Strategies for Inexact Subgraph Matching on Noisy Multiplex Networks IEEE BigData, GTA3 workshop 2019 | A. Kopylov, J. Xu
	G-Finder: Approximate Attributed Subgraph Matching IEEE BigData 2019 | L. Liu, B. Du, J. Xu, H. Tong
	ORIGIN: Non-Rigid Network Alignment IEEE BigData 2019 | S. Zhang, H. Tong, J. Xu, Y. Hu, R. Maciejewski
	Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy [Code] ACM CIKM 2019 | J. Wu and J. He
	Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks ACM CIKM 2019 | Z. Liu, D. Zhou, J. He
	MrMine: Multi-resolution Multi-network Embedding ACM CIKM 2019 | B. Du and H. Tong
	Misc-GAN: A Multi-scale Generative Model for Graphs. Front Big Data 2019 | D. Zhou, L. Zheng, J. Xu, and J. He
	DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification ACM KDD 2019 | J. Wu, J. He, and J. Xu
	Multilevel Network Alignment [Code] ACM WWW 2019 | S. Zhang, H. Tong, R. Maciejewski, T. Eliassi-Rad
	Graph Convolutional Networks: Algorithms, Applications and Open Challenges CSoNet 2018 | S. Zhang, H. Tong, J. Xu and R. Maciejewski
	ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data [Code] IEEE BigData | J. Wu, J. He, and Y. Liu
	SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization ACM KDD 2018 | D. Zhou, J. He, H. Yang, and W. Fan
	FASTEN: Fast Sylvester Equation Solver for Graph Mining ACM KDD2018 | B. Du and H. Tong
	Metric for Evaluating Network Alignment ACM WSDM, GTA3 workshop 2017 | J. Douglas, B. Zimmerman, A. Kopylov, J. Xu, D. Sussman, and V. Lyzinski
	Attributed Network Alignment: Problem Definitions and Fast Solutions [Code] IEEE TKDE 2018 | S. Zhang and H. Tong

Team Members

• Jiejun Xu, HRL (PI)
• Connie Ni, HRL (Research Scientist)
• Alexei Kopylov, HRL (Research Scientist)
• Shane Roache, HRL (Research Scientist)
• Tsai-Ching Lu, HRL (Research Scientist)
• Tyler Derr, MSU (Research Intern)
• Daniel Xie, HRL (Research Intern)
• Hanghang Tong, UIUC (Professor)
• Jingrui He, UIUC (Professor)
• Si Zhang, UIUC (Graduate Researcher)
• Boxin Du, UIUC (Graduate Researcher)
• Dawei Zhou, UIUC (Graduate Researcher)
• Jun Wu, UIUC (Graduate Researcher)
• Lihui Liu, UIUC (Graduate Researcher)
• Yuchen Yan, UIUC (Graduate Researcher)
• Dongqi Fu, UIUC (Graduate Researcher)
• Zhe Xu, UIUC (Graduate Researcher)
• Yao Zhou, UIUC (Graduate Researcher)
• Lecheng Zheng, UIUC (Graduate Researcher)

Media Coverage

12/01/2020 | Complex Analytics of Network of Networks – CANON
01/14/2020 | CANON Enters New Phase to Increase Focus on Adversarial Activity
01/29/2018 | Seeking Weapons of Mass Terrorism in a Haystack of Big Network Data

Contact

Email: media[at]hrl.com

Media Services
HRL Laboratories, LLC
3011 Malibu Canyon Road
Malibu, CA 90265
USA

Acknowledgement

This material is based upon work supported by the United States Air Force and DARPA under contract number FA8750-17-C-0153. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and DARPA.