Mark Heimann

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 37

Top Topics

Top Venues

Publications

Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR (2024)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR (2024)
Samuel Leventhal, Attila Gyulassy, Mark Heimann, Valerio Pascucci
Exploring Classification of Topological Priors With Machine Learning for Feature Extraction. IEEE Trans. Vis. Comput. Graph. 30 (7) (2024)
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. CoRR (2023)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR (2023)
Rakshith Subramanyam, Mark Heimann, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. WACV (2023)
Jiong Zhu, Yujun Yan, Mark Heimann, Lingxiao Zhao, Leman Akoglu, Danai Koutra
Heterophily and Graph Neural Networks: Past, Present and Future. IEEE Data Eng. Bull. 46 (2) (2023)
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG (2023)
Samuel Leventhal, Attila Gyulassy, Valerio Pascucci, Mark Heimann
Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks. ICIP (2023)
Jing Zhu, Danai Koutra, Mark Heimann
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CIKM (2022)
Jing Zhu, Danai Koutra, Mark Heimann
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CoRR (2022)
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Analyzing Data-Centric Properties for Graph Contrastive Learning. NeurIPS (2022)
Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
Toward Understanding and Evaluating Structural Node Embeddings. ACM Trans. Knowl. Discov. Data 16 (3) (2022)
Rakshith Subramanyam, Mark Heimann, Jayram S. Thathachar, Rushil Anirudh, Jayaraman J. Thiagarajan
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. CoRR (2022)
Konstantia Georgouli, Helgi I. Ingólfsson, Fikret Aydin, Mark Heimann, Felice C. Lightstone, Peer-Timo Bremer, Harsh Bhatia
Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints. CoRR (2022)
Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T. Schaub, Danai Koutra
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR (2022)
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Analyzing Data-Centric Properties for Contrastive Learning on Graphs. CoRR (2022)
Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra
Refining Network Alignment to Improve Matched Neighborhood Consistency. SDM (2021)
Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. SDM (2021)
Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
Towards Understanding and Evaluating Structural Node Embeddings. CoRR (2021)
Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra
Refining Network Alignment to Improve Matched Neighborhood Consistency. CoRR (2021)
Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. CoRR (2021)
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
Consistent Network Alignment with Node Embedding. CoRR (2020)
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. NeurIPS (2020)
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. CIKM (2020)
Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CoRR (2020)
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
Generalizing Graph Neural Networks Beyond Homophily. CoRR (2020)
Mark Heimann, Goran Muric, Emilio Ferrara
Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. COMPLEX NETWORKS (2) (2020)
Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CIKM (2020)
Mark Heimann, Tara Safavi, Danai Koutra
Distribution of Node Embeddings as Multiresolution Features for Graphs. ICDM (2019)
Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching. CoRR (2019)
Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra
node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching. ECML/PKDD (1) (2019)
Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, Danai Koutra
Smart Roles: Inferring Professional Roles in Email Networks. KDD (2019)
Yujun Yan, Mark Heimann, Di Jin, Danai Koutra
Fast Flow-based Random Walk with Restart in a Multi-query Setting. SDM (2018)
Mark Heimann, Haoming Shen, Danai Koutra
Node Representation Learning for Multiple Networks: The Case of Graph Alignment. CoRR (2018)
Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
REGAL: Representation Learning-based Graph Alignment. CIKM (2018)
Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen, Danai Koutra
HashAlign: Hash-Based Alignment of Multiple Graphs. PAKDD (3) (2018)