Mark Heimann

According to our database, Mark Heimann authored at least 29 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Exploring Classification of Topological Priors With Machine Learning for Feature Extraction.

Samuel Leventhal, Attila Gyulassy, Mark Heimann, Valerio Pascucci

IEEE Trans. Vis. Comput. Graph., July, 2024

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks.

Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Heterophily and Graph Neural Networks: Past, Present and Future.

Jiong Zhu, Yujun Yan, Mark Heimann, Lingxiao Zhao, Leman Akoglu, Danai Koutra

IEEE Data Eng. Bull., 2023

Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification.

Rakshith Subramanyam, Mark Heimann, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks.

Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks.

Samuel Leventhal, Attila Gyulassy, Valerio Pascucci, Mark Heimann

Proceedings of the IEEE International Conference on Image Processing, 2023

2022

Toward Understanding and Evaluating Structural Node Embeddings.

Junchen Jin, Mark Heimann, Di Jin, Danai Koutra

ACM Trans. Knowl. Discov. Data, 2022

Analyzing Data-Centric Properties for Contrastive Learning on Graphs.

Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan

CoRR, 2022

On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods.

Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra

CoRR, 2022

Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints.

Konstantia Georgouli, Helgi I. Ingólfsson, Fikret Aydin, Mark Heimann, Felice C. Lightstone, Peer-Timo Bremer, Harsh Bhatia

CoRR, 2022

Analyzing Data-Centric Properties for Graph Contrastive Learning.

Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment.

Jing Zhu, Danai Koutra, Mark Heimann

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Towards Understanding and Evaluating Structural Node Embeddings.

Junchen Jin, Mark Heimann, Di Jin, Danai Koutra

CoRR, 2021

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding.

Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Refining Network Alignment to Improve Matched Neighborhood Consistency.

Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

2020

Unsupervised Structural Embedding Methods for Efficient Collective Network Mining.

Mark Heimann

PhD thesis, 2020

Generalizing Graph Neural Networks Beyond Homophily.

Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra

CoRR, 2020

Consistent Network Alignment with Node Embedding.

Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra

CoRR, 2020

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs.

Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales.

Mark Heimann, Goran Muric, Emilio Ferrara

Proceedings of the Complex Networks & Their Applications IX, 2020

G-CREWE: Graph CompREssion With Embedding for Network Alignment.

Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding.

Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2019

node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching.

Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Smart Roles: Inferring Professional Roles in Email Networks.

Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, Danai Koutra

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Distribution of Node Embeddings as Multiresolution Features for Graphs.

Mark Heimann, Tara Safavi, Danai Koutra

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

2018

Node Representation Learning for Multiple Networks: The Case of Graph Alignment.

Mark Heimann, Haoming Shen, Danai Koutra

CoRR, 2018

Fast Flow-based Random Walk with Restart in a Multi-query Setting.

Yujun Yan, Mark Heimann, Di Jin, Danai Koutra

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

HashAlign: Hash-Based Alignment of Multiple Graphs.

Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen, Danai Koutra

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

REGAL: Representation Learning-based Graph Alignment.

Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018