Mark Heimann - RankMe

39 papers A* 5A 9B 1Journal 19Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	Conformal Edge-Weight Prediction in Latent Space. SDM Akash Choudhuri, Yongjian Zhong, Mehrdad Moharrami, Christine Klymko, Mark Heimann, Jayaraman J. Thiagarajan, Bijaya Adhikari
2024	A*	`conf`	Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
2024	J	`jnl`	Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
2024	J	`jnl`	Exploring Classification of Topological Priors With Machine Learning for Feature Extraction. IEEE Trans. Vis. Comput. Graph. Samuel Leventhal, Attila Gyulassy, Mark Heimann, Valerio Pascucci
2023	J	`jnl`	Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
2023	A	`conf`	Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. WACV Rakshith Subramanyam, Mark Heimann, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan
2023	J	`jnl`	Heterophily and Graph Neural Networks: Past, Present and Future. IEEE Data Eng. Bull. Jiong Zhu, Yujun Yan, Mark Heimann, Lingxiao Zhao, Leman Akoglu, Danai Koutra
2023	B	`conf`	Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks. ICIP Samuel Leventhal, Attila Gyulassy, Valerio Pascucci, Mark Heimann
2023	—	`conf`	On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2023	J	`jnl`	On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. CoRR Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2022	J	`jnl`	Analyzing Data-Centric Properties for Contrastive Learning on Graphs. CoRR Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
2022	A*	`conf`	Analyzing Data-Centric Properties for Graph Contrastive Learning. NeurIPS Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
2022	A	`conf`	CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CIKM Jing Zhu, Danai Koutra, Mark Heimann
2022	J	`jnl`	CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CoRR Jing Zhu, Danai Koutra, Mark Heimann
2022	J	`jnl`	Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. CoRR Rakshith Subramanyam, Mark Heimann, Jayram S. Thathachar, Rushil Anirudh, Jayaraman J. Thiagarajan
2022	J	`jnl`	Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints. CoRR Konstantia Georgouli, Helgi I. Ingólfsson, Fikret Aydin, Mark Heimann, Felice C. Lightstone, Peer-Timo Bremer, Harsh Bhatia
2022	J	`jnl`	On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2022	J	`jnl`	Toward Understanding and Evaluating Structural Node Embeddings. ACM Trans. Knowl. Discov. Data Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
2021	A	`conf`	Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. SDM Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
2021	J	`jnl`	Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. CoRR Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
2021	A	`conf`	Refining Network Alignment to Improve Matched Neighborhood Consistency. SDM Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra
2021	J	`jnl`	Refining Network Alignment to Improve Matched Neighborhood Consistency. CoRR Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra
2021	J	`jnl`	Towards Understanding and Evaluating Structural Node Embeddings. CoRR Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
2020	A*	`conf`	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. NeurIPS Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
2020	A	`conf`	CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. CIKM Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
2020	J	`jnl`	Consistent Network Alignment with Node Embedding. CoRR Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
2020	A	`conf`	G-CREWE: Graph CompREssion With Embedding for Network Alignment. CIKM Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
2020	J	`jnl`	G-CREWE: Graph CompREssion With Embedding for Network Alignment. CoRR Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
2020	J	`jnl`	Generalizing Graph Neural Networks Beyond Homophily. CoRR Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
2020	—	`conf`	Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. COMPLEX NETWORKS (2) Mark Heimann, Goran Muric, Emilio Ferrara
2020	—	`—`	Unsupervised Structural Embedding Methods for Efficient Collective Network Mining. Mark Heimann
2019	A*	`conf`	Distribution of Node Embeddings as Multiresolution Features for Graphs. ICDM Mark Heimann, Tara Safavi, Danai Koutra
2019	A*	`conf`	Smart Roles: Inferring Professional Roles in Email Networks. KDD Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, Danai Koutra
2019	—	`conf`	node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching. ECML/PKDD (1) Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra
2019	J	`jnl`	node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching. CoRR Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra
2018	A	`conf`	Fast Flow-based Random Walk with Restart in a Multi-query Setting. SDM Yujun Yan, Mark Heimann, Di Jin, Danai Koutra
2018	—	`conf`	HashAlign: Hash-Based Alignment of Multiple Graphs. PAKDD (3) Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen, Danai Koutra
2018	J	`jnl`	Node Representation Learning for Multiple Networks: The Case of Graph Alignment. CoRR Mark Heimann, Haoming Shen, Danai Koutra
2018	A	`conf`	REGAL: Representation Learning-based Graph Alignment. CIKM Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra