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Soji Adeshina
Soji Adeshina
Applied Scientist
Verified email at amazon.com
Title
Cited by
Cited by
Year
PaGE-Link: Path-based graph neural network explanation for heterogeneous link prediction
S Zhang, J Zhang, X Song, S Adeshina, D Zheng, C Faloutsos, Y Sun
Proceedings of the ACM Web Conference 2023, 3784-3793, 2023
282023
Train your own gnn teacher: Graph-aware distillation on textual graphs
C Mavromatis, VN Ioannidis, S Wang, D Zheng, S Adeshina, J Ma, H Zhao, ...
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023
252023
Does your graph need a confidence boost? convergent boosted smoothing on graphs with tabular node features
J Chen, J Mueller, VN Ioannidis, S Adeshina, Y Wang, T Goldstein, D Wipf
162021
Relational graph neural networks for fraud detection in a super-app environment
JD Acevedo-Viloria, L Roa, S Adeshina, CC Olazo, A Rodríguez-Rey, ...
arXiv preprint arXiv:2107.13673, 2021
122021
Convergent boosted smoothing for modeling graph data with tabular node features
J Chen, J Mueller, VN Ioannidis, S Adeshina, Y Wang, T Goldstein, D Wipf
arXiv preprint arXiv:2110.13413, 2021
52021
Orthoreg: Improving graph-regularized mlps via orthogonality regularization
H Zhang, S Wang, VN Ioannidis, S Adeshina, J Zhang, X Qin, C Faloutsos, ...
arXiv preprint arXiv:2302.00109, 2023
42023
Scalable consistency training for graph neural networks via self-ensemble self-distillation
C Hawkins, VN Ioannidis, S Adeshina, G Karypis
arXiv preprint arXiv:2110.06290, 2021
22021
Credit risk modeling with graph machine learning
S Das, X Huang, S Adeshina, P Yang, L Bachega
INFORMS Journal on Data Science 2 (2), 197-217, 2023
12023
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
Z Dai, V Ioannidis, S Adeshina, Z Jost, C Faloutsos, G Karypis
arXiv preprint arXiv:2206.04255, 2022
12022
Revisit Orthogonality in Graph-Regularized MLPs
H Zhang, S Wang, VN Ioannidis, S Adeshina, J Zhang, X Qin, C Faloutsos, ...
Proceedings of the 33rd ACM International Conference on Information and …, 2024
2024
GraphStorm: all-in-one graph machine learning framework for industry applications
D Zheng, X Song, Q Zhu, J Zhang, T Vasiloudis, R Ma, H Zhang, Z Wang, ...
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024
2024
Hierarchical Compression of Text-Rich Graphs via Large Language Models
S Zhang, D Zheng, J Zhang, Q Zhu, S Adeshina, C Faloutsos, G Karypis, ...
arXiv preprint arXiv:2406.11884, 2024
2024
NETINFOF framework: Measuring and exploiting network usable information
MC Lee, H Yu, J Zhang, VN Ioannidis, X Song, S Adeshina, D Zheng, ...
arXiv preprint arXiv:2402.07999, 2024
2024
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
DAI Zhenwei, V Ioannidis, S Adeshina, Z Jost, C Faloutsos, G Karypis
Learning on Graphs Conference, 35: 1-35: 15, 2022
2022
PropInit: Scalable Inductive Initialization for Heterogeneous Graph Neural Networks
S Adeshina, J Zhang, M Kim, M Chen, R Fathony, A Vashisht, J Chen, ...
2022 IEEE International Conference on Knowledge Graph (ICKG), 6-13, 2022
2022
Conditional invariances for conformer invariant protein representations
B Srinivasan, VN Ioannidis, S Adeshina, M Kakodkar, G Karypis, ...
2022
Agent-G: An Agentic Framework for Graph Retrieval Augmented Generation
MC Lee, Q Zhu, C Mavromatis, Z Han, S Adeshina, VN Ioannidis, ...
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