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Mark Heimann
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2020 – today
- 2024
- [j3]Samuel Leventhal
, Attila Gyulassy
Exploring Classification of Topological Priors With Machine Learning for Feature Extraction. IEEE Trans. Vis. Comput. Graph. 30(7): 3959-3972 (2024) - [c18]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR 2024 - [i16]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR abs/2401.03350 (2024) - 2023
- [j2]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): 12-34 (2023) - [c17]Samuel Leventhal, Attila Gyulassy, Valerio Pascucci
Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks. ICIP 2023: 2995-2999 - [c16]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: 6 - [c15]Rakshith Subramanyam, Mark Heimann, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan:
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. WACV 2023: 2478-2486 - [i15]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 abs/2306.05557 (2023) - [i14]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR abs/2309.10976 (2023) - 2022
- [j1]Junchen Jin, Mark Heimann, Di Jin, Danai Koutra:
Toward Understanding and Evaluating Structural Node Embeddings. ACM Trans. Knowl. Discov. Data 16(3): 58:1-58:32 (2022) - [c14]Jing Zhu, Danai Koutra, Mark Heimann:
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CIKM 2022: 4747-4751 - [c13]Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan:
Analyzing Data-Centric Properties for Graph Contrastive Learning. NeurIPS 2022 - [i13]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 abs/2207.04333 (2022) - [i12]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR abs/2207.04376 (2022) - [i11]Rakshith Subramanyam, Mark Heimann, Jayram S. Thathachar, Rushil Anirudh, Jayaraman J. Thiagarajan:
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. CoRR abs/2207.12346 (2022) - [i10]Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan:
Analyzing Data-Centric Properties for Contrastive Learning on Graphs. CoRR abs/2208.02810 (2022) - [i9]Jing Zhu, Danai Koutra, Mark Heimann:
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CoRR abs/2208.10682 (2022) - 2021
- [c12]Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra:
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. SDM 2021: 163-171 - [c11]Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra:
Refining Network Alignment to Improve Matched Neighborhood Consistency. SDM 2021: 172-180 - [i8]Junchen Jin, Mark Heimann, Di Jin, Danai Koutra:
Towards Understanding and Evaluating Structural Node Embeddings. CoRR abs/2101.05730 (2021) - [i7]Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra:
Refining Network Alignment to Improve Matched Neighborhood Consistency. CoRR abs/2101.08808 (2021) - [i6]Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra:
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. CoRR abs/2102.13582 (2021) - 2020
- [b1]Mark Heimann:
Unsupervised Structural Embedding Methods for Efficient Collective Network Mining. University of Michigan, USA, 2020 - [c10]Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CIKM 2020: 1255-1264 - [c9]Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra:
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. CIKM 2020: 1985-1988 - [c8]Mark Heimann, Goran Muric, Emilio Ferrara
Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. COMPLEX NETWORKS (2) 2020: 3-14 - [c7]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 - [i5]Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra:
Consistent Network Alignment with Node Embedding. CoRR abs/2005.04725 (2020) - [i4]Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra:
Generalizing Graph Neural Networks Beyond Homophily. CoRR abs/2006.11468 (2020) - [i3]Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra:
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CoRR abs/2007.16208 (2020)
2010 – 2019
- 2019
- [c6]Mark Heimann, Tara Safavi, Danai Koutra:
Distribution of Node Embeddings as Multiresolution Features for Graphs. ICDM 2019: 289-298 - [c5]Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, Danai Koutra:
Smart Roles: Inferring Professional Roles in Email Networks. KDD 2019: 2923-2933 - [c4]Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra:
node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching. ECML/PKDD (1) 2019: 483-506 - [i2]Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra:
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching. CoRR abs/1904.08572 (2019) - 2018
- [c3]Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra:
REGAL: Representation Learning-based Graph Alignment. CIKM 2018: 117-126 - [c2]Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen, Danai Koutra:
HashAlign: Hash-Based Alignment of Multiple Graphs. PAKDD (3) 2018: 726-739 - [c1]Yujun Yan
Fast Flow-based Random Walk with Restart in a Multi-query Setting. SDM 2018: 342-350 - [i1]Mark Heimann, Haoming Shen, Danai Koutra:
Node Representation Learning for Multiple Networks: The Case of Graph Alignment. CoRR abs/1802.06257 (2018)
Coauthor Index
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last updated on 2024-08-08 19:14 CEST by the dblp team
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