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Mark Heimann
Mark Heimann
Lawrence Livermore National Laboratory
Verifierad e-postadress på umich.edu - Startsida
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Beyond homophily in graph neural networks: Current limitations and effective designs
J Zhu, Y Yan, L Zhao, M Heimann, L Akoglu, D Koutra
Proceedings of the 34th Annual Conference on Neural Information Processing …, 2020
7512020
Regal: Representation learning-based graph alignment
M Heimann, H Shen, T Safavi, D Koutra
Proceedings of the 27th ACM international conference on information and …, 2018
2972018
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding
X Chen, M Heimann, F Vahedian, D Koutra
Proceedings of the 29th ACM International Conference on Information …, 2020
522020
node2bits: Compact Time-and Attribute-aware Node Representations for User Stitching
D Jin, M Heimann, R Rossi, D Koutra
ECML/PKDD European Conference on Principles and Practice of Knowledge …, 2019
442019
On generalizing neural node embedding methods to multi-network problems
M Heimann, D Koutra
KDD MLG Workshop 58, 2017
352017
G-crewe: Graph compression with embedding for network alignment
KK Qin, FD Salim, Y Ren, W Shao, M Heimann, D Koutra
Proceedings of the 29th ACM International Conference on Information …, 2020
292020
Toward understanding and evaluating structural node embeddings
J Jin, M Heimann, D Jin, D Koutra
ACM Transactions on Knowledge Discovery from Data (TKDD) 16 (3), 1-32, 2021
25*2021
Hashalign: Hash-based alignment of multiple graphs
M Heimann, W Lee, S Pan, KY Chen, D Koutra
Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia …, 2018
242018
Smart Roles: Inferring Professional Roles in Email Networks
D Jin, M Heimann, T Safavi, M Wang, W Lee, L Snider, D Koutra
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
222019
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
J Zhu, X Lu, M Heimann, D Koutra
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021
202021
Distribution of Node Embeddings as Multiresolution Features for Graphs
M Heimann, T Safavi, D Koutra
Proceedings of the 19th IEEE International Conference on Data Mining (ICDM), 2019
192019
Analyzing data-centric properties for graph contrastive learning
P Trivedi, ES Lubana, M Heimann, D Koutra, J Thiagarajan
Advances in Neural Information Processing Systems 35, 14030-14043, 2022
14*2022
Refining network alignment to improve matched neighborhood consistency
M Heimann, X Chen, F Vahedian, D Koutra
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021
102021
On graph neural network fairness in the presence of heterophilous neighborhoods
D Loveland, J Zhu, M Heimann, B Fish, MT Schaub, D Koutra
arXiv preprint arXiv:2207.04376, 2022
7*2022
Fast flow-based random walk with restart in a multi-query setting
Y Yan, M Heimann, D Jin, D Koutra
Proceedings of the 2018 SIAM International Conference on Data Mining, 342-350, 2018
62018
Caper: Coarsen, align, project, refine-a general multilevel framework for network alignment
J Zhu, D Koutra, M Heimann
Proceedings of the 31st ACM International Conference on Information …, 2022
52022
Exploring classification of topological priors with machine learning for feature extraction
S Leventhal, A Gyulassy, M Heimann, V Pascucci
IEEE Transactions on Visualization and Computer Graphics, 2023
42023
Heterophily and graph neural networks: Past, present and future
J Zhu, Y Yan, M Heimann, L Zhao, L Akoglu, D Koutra
IEEE Data Engineering Bulletin, 2023
32023
Structural node embedding in signed social networks: Finding online misbehavior at multiple scales
M Heimann, G Murić, E Ferrara
Complex Networks & Their Applications IX: Volume 2, Proceedings of the Ninth …, 2021
32021
Contrastive knowledge-augmented meta-learning for few-shot classification
R Subramanyam, M Heimann, TS Jayram, R Anirudh, JJ Thiagarajan
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023
12023
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Artiklar 1–20