Mark Heimann
I am a researcher in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. In 2020, I received my PhD in computer science from the University of Michigan, where I was a member of the GEMS Lab and advised by Danai Koutra. During my PhD I also completed internships at the Information Sciences Institute, Adobe Research and Oak Ridge National Laboratory. I completed my undergraduate degree at Washington University in St. Louis in 2015.
My research is in machine learning for graph or network-structured data. You can read more about my PhD work using node and graph level embeddings in technical detail in my dissertation, or more quickly consult a conceptual confectionary conspectus in the dessertation I made to celebrate my dissertation defense:) At Lawrence Livermore National Laboratory, I have worked on new graph neural network methods and applications to molecular modeling, scientific image segmentation, and software analysis.
Publications
- Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T Schaub, and Danai Koutra. On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LOG 2023.
- Samuel Leventhal, Attila Gyulassy, Valerio Pascucci, and Mark Heimann. Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks. ICIP 2023. Also accepted at GLFrontiers at NeurIPS 2022.
- Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, and Jayaraman Thiagarajan. On Estimating the Epistemic Uncertainty of Graph Neural Networks using Stochastic Centering. DMLR @ ICML 2023. Also accepted at GLFrontiers at NeurIPS 2023.
- Samuel Leventhal, Attila Gyulassy, Mark Heimann, and Valerio Pascucci. Exploring Classification of Topological Priors with Machine Learning for Feature Extraction.” TVCG 2023.
- Rakshith Subramanyam, Mark Heimann, Jayram Thathachar, Rushil Anirudh, and Jayaraman J. Thiagarajan. Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. WACV 2023.
- Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, and Jayaraman Thiagarajan. Understanding Self-Supervised Graph Representation Learning from a Data-Centric Perspective. NeurIPS 2022. Also accepted at GLB @ WebConf 2022, MLG @ KDD 2022.
- Jing Zhu, Danai Koutra, and Mark Heimann. CAPER: Coarsen, Align, Project, Refine – A General Multilevel Framework for Network Alignment. CIKM 2022.
- Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael Schaub, and Danai Koutra. On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. DLG @ KDD, 2022.
- Konstantia Georgouli, Helgi I. Ingólfsson, Fikret Aydin, Mark Heimann, Felice Lightstone, Peer-Timo Bremer, Harsh Bhatia. Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints. CompBio @ ICML 2022.
- Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, and Danai Koutra. Refining Network Alignment to Improve Matched Neighborhood Consistency. SIAM International Conference on Data Mining (SDM), 2021. [code]
- Jing Zhu*, Xingyu Lu*, Mark Heimann, and Danai Koutra. Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. SIAM International Conference on Data Mining (SDM), 2021. [code]
- Junchen Jin, Mark Heimann, Di Jin, and Danai Koutra. Towards Understanding and Evaluating Structural Node Embedding. TKDD 2021. [code] Also invited for contributed talk at KDD MLG 2020 [code] [video]
- Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Conference on Neural Information Processing Systems (NeurIPS), 2020. [code]
- Mark Heimann, Goran Murić, and Emilio Ferrara. Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. International Conference on Complex Networks and their Applications (Complex Networks), 2020. [code]
- Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, and Danai Koutra. CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. International Conference on Information and Knowledge Management (CIKM), 2020. Also accepted at KDD MLG Workshop 2020 [code] [video]
- Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann and Danai Koutra. G-CREWE: Graph CompREssion With Embedding for Network Alignment. International Conference on Information and Knowledge Management (CIKM), 2020. [code]
- Mark Heimann, Tara Safavi, and Danai Koutra. Distribution of Node Embeddings as Multiresolution Features for Graphs. IEEE International Conference on Data Mining (ICDM), 2019. Best Student Paper [code]
- Di Jin, Mark Heimann, Ryan Rossi, and Danai Koutra. node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching. European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2019. [code]
- Di Jin*, Mark Heimann*, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, and Danai Koutra. Smart Roles: Inferring Professional Roles in Email Networks. Conference on Knowledge Discovery and Data Mining (KDD), 2019. [code]
- Mark Heimann, Haoming Shen, Tara Safavi, and Danai Koutra. REGAL: Representation Learning-based Graph Alignment. International Conference on Information and Knowledge Management (CIKM), 2018. [code]
- Mark Heimann*, Wei Lee*, Shengjie Pan, Kuan-Yu Chen, and Danai Koutra. HashAlign: Hash-Based Alignment of Multiple Graphs. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018. [code]
- Yujun Yan, Mark Heimann, Di Jin, and Danai Koutra. Fast Flow-based Random Walk with Restart in a Multi-query Setting. SIAM International Conference on Data Mining (SDM), 2018. [code]
- Mark Heimann and Danai Koutra. On Generalizing Neural Node Embedding Methods to Multi-Network Problems. KDD Workshop on Mining and Learning with Graphs (MLG), 2017.