REGAL: Representation Learning-based Graph Alignment. - Mark Heimann; Haoming Shen; Tara Safavi; Danai Koutra (CIKM, 2018)

HashAlign: Hash-Based Alignment of Multiple Graphs. - Mark Heimann; Wei Lee; Shengjie Pan; Kuan-Yu Chen; Danai Koutra (PAKDD (3), 2018)

Fast Flow-based Random Walk with Restart in a Multi-query Setting. - Yujun Yan; Mark Heimann; Di Jin 003; Danai Koutra (SDM, 2018)

Node Representation Learning for Multiple Networks: The Case of Graph Alignment. - Mark Heimann; Haoming Shen; Danai Koutra (CoRR, 2018)

Distribution of Node Embeddings as Multiresolution Features for Graphs. - Mark Heimann; Tara Safavi; Danai Koutra (ICDM, 2019)

Smart Roles: Inferring Professional Roles in Email Networks. - Di Jin 003; Mark Heimann; Tara Safavi; Mengdi Wang; Wei Lee; Lindsay Snider; Danai Koutra (KDD, 2019)

node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching. - Di Jin 003; Mark Heimann; Ryan A. Rossi; Danai Koutra (ECML/PKDD (1), 2019)

CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. - Xiyuan Chen; Mark Heimann; Fatemeh Vahedian; Danai Koutra (CIKM, 2020)

G-CREWE: Graph CompREssion With Embedding for Network Alignment. - Kyle Kai Qin; Flora D. Salim; Yongli Ren; Wei Shao 006; Mark Heimann; Danai Koutra (CIKM, 2020)

Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. - Mark Heimann; Goran Muric; Emilio Ferrara (COMPLEX NETWORKS (2), 2020)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. - Jiong Zhu; Yujun Yan; Lingxiao Zhao; Mark Heimann; Leman Akoglu; Danai Koutra (NeurIPS, 2020)

Consistent Network Alignment with Node Embedding. - Xiyuan Chen; Mark Heimann; Fatemeh Vahedian; Danai Koutra (CoRR, 2020)

Generalizing Graph Neural Networks Beyond Homophily. - Jiong Zhu; Yujun Yan; Lingxiao Zhao; Mark Heimann; Leman Akoglu; Danai Koutra (CoRR, 2020)

Unsupervised Structural Embedding Methods for Efficient Collective Network Mining. - Mark Heimann (2020)

Refining Network Alignment to Improve Matched Neighborhood Consistency. - Mark Heimann; Xiyuan Chen; Fatemeh Vahedian; Danai Koutra (SDM, 2021)

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. - Jing Zhu 005; Xingyu Lu; Mark Heimann; Danai Koutra (SDM, 2021)

Towards Understanding and Evaluating Structural Node Embeddings. - Junchen Jin; Mark Heimann; Di Jin 003; Danai Koutra (CoRR, 2021)

CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. - Jing Zhu 005; Danai Koutra; Mark Heimann (CIKM, 2022)

Analyzing Data-Centric Properties for Graph Contrastive Learning. - Puja Trivedi; Ekdeep Singh Lubana; Mark Heimann; Danai Koutra; Jayaraman J. Thiagarajan (NeurIPS, 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)

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)

Analyzing Data-Centric Properties for Contrastive Learning on Graphs. - Puja Trivedi; Ekdeep Singh Lubana; Mark Heimann; Danai Koutra; Jayaraman J. Thiagarajan (CoRR, 2022)

Toward Understanding and Evaluating Structural Node Embeddings. - Junchen Jin; Mark Heimann; Di Jin 003; Danai Koutra (ACM Trans. Knowl. Discov. Data, 2022)

Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks. - Samuel Leventhal; Attila Gyulassy; Valerio Pascucci; Mark Heimann (ICIP, 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 (LoG, 2023)

Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. - Rakshith Subramanyam; Mark Heimann; T. S. Jayram; Rushil Anirudh; Jayaraman J. Thiagarajan (WACV, 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)

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. - Puja Trivedi; Mark Heimann; Rushil Anirudh; Danai Koutra; Jayaraman J. Thiagarajan (ICLR, 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., 2024)

Conformal Edge-Weight Prediction in Latent Space. - Akash Choudhuri; Yongjian Zhong; Mehrdad Moharrami; Christine Klymko; Mark Heimann; Jayaraman J. Thiagarajan; Bijaya Adhikari (SDM, 2025)