Email for my updated CV at ioann006@umn.edu
See also google scholar for my updated publication list: link
Journals
[J11] J. Chen, J. Mueller, V. N. Ioannidis, T. Goldstein, D. Wipf “Graph Neural Networks Formed via Layer- wise Ensembles of Heterogeneous Base Models,”, Trans. on Machine Learning Research, Feb. 2024.
[J10] Z. Chen, B. Peng, V. N. Ioannidis, M. Li, G. Karypis, and X. Ning \CTKG: A Knowledge Graph for Clinical Trials," Nature Scientific Reports, Feb. 2022.
[J9] V. N. Ioannidis, X. Song, S. Manchanda, M. Li, X. Pan, D. Zheng, X. Ning, X. Zeng, G. Karypis, \DRKG - Drug Repurposing Knowledge Graph for Covid-19", Biomedical Journal, Jun. 2020 (submitted), see github.com/gnn4dr/DRKG.
[J8] V. N. Ioannidis, S. Chen, and G. B. Giannakis, Efficient and Stable Graph Scattering Transforms via Pruning," IEEE Trans. on Pattern Analysis and Machine Intelligence, Jan. 2021, published.
[J7] V. N. Ioannidis, A. G. Marques, and G. B. Giannakis, \Tensor Graph Convolutional Networks for Multi-relational and Robust Learning," IEEE Trans. on Sig. Processing, Jan. 2020.
[J6] V. N. Ioannidis, and G. B. Giannakis, \GraphSAC: Robustifying learning and unveiling anomalies in large-scale graphs," IEEE Trans. on Pattern Analysis and Machine Intelligence, Jan. 2020 (submitted).
[J5] Q. Lu, V. N. Ioannidis and G. B. Giannakis, \Graph-adaptive semi-supervised tracking of dynamic processes over switching network modes," IEEE Trans. on Sig. Processing, Mar. 2020.
[J4] V. N. Ioannidis, A. S. Zamzam, G. B. Giannakis and N. D. Sidiropoulos, \Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection," IEEE Trans. on Knowledge and Data Engineering, May 2019.
[J3] V. N. Ioannidis, Y. Shen, and G. B. Giannakis, \Semi-Blind Inference of Topologies and Dynamical Processes over Graphs," IEEE Trans. on Sig. Processing, vol. 67, no. 9, pp. 2263-2274, May 2019.
[J2] V. N. Ioannidis, D. Romero, and G. B. Giannakis, \Learning dynamic processes over dynamic graphs via multi-kernel kriged Kalman filter," IEEE Trans. on Sig. Processing, vol. 66, no. 12, pp. 3228-3239, June 2018.
[J1] D. Romero, V. N. Ioannidis, and G. B. Giannakis, \Kernel-based Reconstruction and Kalman Filtering of Space-time Functions on Dynamic Graphs," IEEE Journal on Selected Topics in Sig. Processing, vol. 11, no. 6, pp. 856 - 869, Sept. 2017.
Book chapter
BC1. V. N. Ioannidis, M. Ma, A. Nikolakopoulos, and G. B. Giannakis, "Kernel-based Inference of Functions on Graphs," Adaptive Learning Methods for Nonlinear System Modeling, D. Comminiello and J. Principe Eds., Elsevier, 2018.
Conferences
[C50] J. Chen, H. Zhang, S. Yun, A. Mottini, R. Ying, X. song, V. N. Ioannidis, Z. Li, Q. cui “GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models.” in EMNLP Nov. 2025
[C49] C. Mavromatis, S. Adeshina, V. N. Ioannidis, Z. Han, Q. Zhu, I. Robinson, B. Thompson, H. Rangwala, and G. Karypis. ”BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering.” in EMNLP Nov. 2025.
[C48] M.-C. Lee, Q. Zhu, C. Mavromatis, Z. Han, S. Adeshina, V. N. Ioannidis, H. Rangwala, C. Faloutsos, “HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases,” in ACL Aug. 2025
[C47] A. Ghassel, I. Robinson, G. Tanase, H. Cooper, B. Thompson, Z. Han, V. N. Ioannidis, S. Adeshina, Huzefa Rangwala, “Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval,” in KDD Aug. 2025
[C45] K. Grover, H. Yu, X. Song, Q. Zhu, H. Xie, V. N. Ioannidis, C. Faloutsos, “Spectro-Riemannian Graph Neural Networks,” ICLR May 2025
[C44] S. Wu, S. Zhao, M. Yasunaga, K. Huang, K. Cao, Q. Huang, V. N. Ioannidis, K. Subbian, J. Zou and J. Leskovec “STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases”, in NeurIPS Dec. 2024.
[C43] S. Wu, S. Zhao, Q. Huang, K. Huang, M. Yasunaga, K. Cao, V. N. Ioannidis, K. Subbian, J. Leskovec, J. Zou, “Avatar: Optimizing LLM Agents for Tool-Assisted Knowledge Retrieval”, in NeurIPS Dec. 2024.
[C42] H. Zhang, S. Wang, V. N. Ioannidis, J. Zhang, X. Qin, C. Faloutsos, D. Zheng, G. Karypis, P. S Yu “OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization,” at Conference on Information and Knowledge Management (CIKM), Oct. 2024.
[C41] J. Zhu, X. Song, V. N. Ioannidis, C. Faloutsos, D. Koutra, “TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning,” SIGIR conference on R&D in Information Retrieval, July 2024
[C40] J. Zhu, Y. Zhou , V. N. Ioannidis, S. Qian, W. Ai, X. Song, D. Koutra, “Pitfalls in link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices,” at Web Search and Data Mining, (WSDM), Best Paper Award, March 2024
[C39] Z. Wang, Z. Wang, B. Srinivasan, V. N. Ioannidis, H. Rangwala, R. Anubhai, “BioBRIDGE: Bridging Biomedical Foundation Models via Knowledge Graph,” ICLR May 2024
[C38] M.-C. Lee, V. N. Ioannidis, J.Zhang, C. Faloutsos, “NETINFOF: Detection and exploitation of network usable information,” ICLR spotlight May 2024
[C37] S. Tipirneni, R. Adkathimar, N. Choudhary, G. Hiranandani, R. A. Amjad, V. N. Ioannidis, C. Yuan, C. K. Reddy, “Large Language Model Guided Graph Clustering”, submitted May 2024.
[C36] P. Trivedi, N. Choudhary, E. W. Huang, V. N. Ioannidis, K. Subbian, D. Koutra, “Context-Aware Clustering using Large Language Models”, submitted May 2024.
[C35] J. Zhu, X. Song, V. N. Ioannidis, C. Faloutsos, D. Koutra, “TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning,” Knowledge4NLP at KDD conference Aug. 2023
[C34] Y. Wu, R. A. Barton, Z. Wang, V. N. Ioannidis, C. De Donno, L. C. Price, L. F. Voloch, G. Karypis \Predicting Cellular Responses with Variational Causal Inference and Re ned Relational Information," published in ICLR, Int. Conf. in Learn. Rep., May 2023.
[C33] C. Mavromatis, V. N. Ioannidis, S. Wang, S. Adeshina, J. Ma, D. Zheng, H. Zhao, G. Karypis, C. Faloutsos \Train Your Own GNN Teacher: Fine-Tuning BERT on Graphs via Graph-Aware Distillation," published in ECML PKDD, Sep. 2023.
[C32] K. Kong, Z. Jost, J. Zhang, V. N. Ioannidis, C. Faloutsos, \Inductive Node Classification on Featureless Heterogeneous Graphs," submitted, Feb 2023.
[C31] J. Zhu, Y. Zhou, V. N. Ioannidis, S. Qian, W. Ai, X. song, D. Koutra, \SpotTarget: Rethinking the Effect of Target Edges in Graph Neural Networks," submitted, Feb 2023.
[C30] H. Xie, D. Zheng, J. Ma, H. Zhang, V. N. Ioannidis, X. Song, Q. Ping, S. Wang, C. Yang, Y. Xu, B. Zeng, T. Chilimbi, \Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications," published in KDD, Aug. 2023.
[C29] Y. Wu, L. C. Price, Z. Wang, V. N. Ioannidis, G. Karypis \Variational causal inference," published in Causality for Real-world impact at NeurIPS in 2022
[C28] B. Srinivasan, V. N. Ioannidis, S. Adeshina, M. Kakodkar, G. Karypis, B. Ribeiro, \Conditional invariances for protein representations," published in Machine Learning for Structural Biology at NeurIPS 2022
[C27] V. N. Ioannidis, X. Song, D. Zheng, H. Zhang, J. Ma, Y. Xu, B. Zeng, T. Chilimbi, G. Karypis, \Efficient and effective training of language and graph neural network models," in Knowledge4NLP at AAAI, Conf. on Artificial Intelligence Feb. 2023
[C26] J. Chen, J. Mueller, V. N. Ioannidis, T. Goldstein, D. Wipf \A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features," submitted June 2022.
[C25] Z. Dai, V. N. Ioannidis, S. Adeshina, Z. Jost, C. Faloutsos, G. Karypis, \ScatterSample: Diversified Label Sampling for Data E cient Graph Neural Network Learning," published in Learning on Graphs conference 2022
[C24] H. Zhou, D. Zheng, I. Nisa, V. N. Ioannidis, X. Song, G. Karypis, \TGL: A general framework for temporal GNN training on billion-scale graphs," published in VLDB 2022
[C23] C. Mavromatis, P. L. Subramanyam, V. N. Ioannidis, S. Adeshina, P. R. Howard, T. Grinberg, N. Hakim, and G. Karypis \TempoQR: Temporal Question Reasoning over Knowledge Graphs," published in AAAI, Conf. on Arti cial Intelligence, Feb 2022.
[C22] J. Chen, J. Mueller, V. N. Ioannidis, S. Adeshina, Y. Wang, T. Goldstein, and D. Wipf \Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features," published in ICLR, Int. Conf. in Learn. Rep., May 2022.
[C21] C. Hawkins, V. N. Ioannidis, S. Adeshina, and G. Karypis \Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation," published in AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications, Feb. 2022.
[C20] V. N. Ioannidis, D. Zheng, and G. Karypis \PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs," published in KDD Workshop on Deep Learning on Graphs: Methods and Applications, Jun. 2020.
[C19] V. N. Ioannidis, D. K. Berberidis, and G. B. Giannakis \Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs," published in IEEE ICASSP, May 2021.
[C18] C. Wise, V. N. Ioannidis, M. R. Calvo, X. Song, G. Price, N. Kulkarni, R. Brand, P. Bhatia and G. Karypis \COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature," published in Conf. on Information and Knowledge Management CIKM (submitted), Jun. 2020.
[C17] V. N. Ioannidis, D. Zheng, and G. Karypis \Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing," published in ICML Workshop on Graph Representation Learning and Beyond, Jun. 2020.
[C16] V. N. Ioannidis, S. Chen, and G. B. Giannakis \Pruned non-trainable Deep Graph Convolutional networks," published in ICLR, Int. Conf. in Learn. Rep., May 2020.
[C15] K. D. Polyzos, C. Mavromatis, V. N. Ioannidis, and G. B. Giannakis, \Unveiling anomalous edges and nominal connectivity of attributed networks," IEEE ICASSP, May 2020.
[C14] V. N. Ioannidis, and G. B. Giannakis, \Defending Graph Convolutional Networks against Adversarial Attacks," IEEE ICASSP, May 2020.
[C13] Q. Lu, V. N. Ioannidis, and G. B. Giannakis, \Semi-supervised learning of processes over multi-relational graphs," IEEE ICASSP, May 2020.
[C12] V. N. Ioannidis, and G. B. Giannakis \Edge Dithering for Robust Adaptive Graph Convolutional Networks," AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications (DLGMA'20), Feb. 2020.
[C11] V. N. Ioannidis, A. G. Marques, and G. B. Giannakis \Graph Neural Networks for Predicting Protein Functions," CAMSAP Conf., Dec. 2019.
[C10] Q. Lu, V. N. Ioannidis, and G. B. Giannakis, \Learning Graph Processes with Multiple Dynamical Models," Asilomar Conf., Nov. 2019.
[C9] Q. Lu, V. N. Ioannidis, and G. B. Giannakis, \Semi-supervised Tracking of Dynamic Processes over Switching Graphs," IEEE Data Science Workshop, June 2019.
[C8] V. N. Ioannidis, A. G. Marques, and G. B. Giannakis \A recurrent graph neural network for multi-relational data," IEEE ICASSP, May 2019.
[C7] V. N. Ioannidis, A. S. Zamzam, G. B. Giannakis, and N. D. Sidiropoulos, \Imputation of coupled tensors and graphs," IEEE GlobalSIP, Nov. 2018.
[C6] V. N. Ioannidis, P. A. Traganitis, Y. Shen, and G. B. Giannakis, \Kernel-based learning of processes over multi-layer graphs," IEEE SPAWC, June 2018.
[C5] V. N. Ioannidis, Y. Shen, and G. B. Giannakis, \Semi-blind inference of topologies and signals over graphs," IEEE Data Science Workshop, June 2018.
[C4] V. N. Ioannidis, A. N. Nikolakopoulos, and G. B. Giannakis, \Semi-parametric kernel-based reconstruction," IEEE GlobalSIP, Nov. 2017.
[C3] V. N. Ioannidis, D. Romero, and G. B. Giannakis, \Inference of spatiotemporal processes over dynamic graphs via kernel kriged kalman lters," Eusipco, Aug. 2017.
[C2] V. N. Ioannidis, D. Romero, and G. B. Giannakis, \Kernel-based reconstruction of space-time functions via extended graphs," Asilomar Conf, Nov. 2016.
[C1] A. S. Zamzam, V. N. Ioannidis, N. D. Sidiropoulos, \Coupled graph tensor factorization," Asilomar Conf, Nov. 2016.