Danai Koutra
Associate Professor, EECS – Computer Science and Engineering
Studies data mining and graph learning, including graph summarization, network alignment and large-scale graph analysis.
Danai Koutra's lab at Michigan works on graph mining & networks, data management & big data and trustworthy & safe ML. 43 papers since 2023, 34 at top venues, mostly KDD and ICLR. 6 PhD students and 2 master's students. 46 alumni. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 79%
- PhD students
- 6
- MS students
- 2
- alumni listed
- 46
Papers by yearfrom DBLP, 2023–2026
- 8 papers2026 · T1 7 · collecting
- 18 papers2025 · T1 14
- 9 papers2024 · T1 8
- 8 papers2023 · T1 5
T1 venuesOtherVenues: KDD, ICLR, WWW, AAAI, AISTATS
Research focuspaper titles that use each topic's words
- Graph mining & networks21 papers
- graph21 papers
- Data management & big data1 paper
- Trustworthy & safe ML1 paper
- studies data0 papers
Paper list
From DBLP.
20268 papers
- WWW (T1)AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents (opens in a new tab)
- SIGIR (T1)Beyond Unimodal Perspectives: Generative Retrieval with Multimodal Semantics (opens in a new tab)
- ICLR (T1)Canonical Tree Cover Neural Networks for Expressive and Invariant Graph Learning (opens in a new tab)
- KDD (T1)Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning (opens in a new tab)
- ICLR (T1)Glance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion (opens in a new tab)
- AAAI (T1)GraphTextack: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs (opens in a new tab)
- KDD (T1)The Second Workshop on SciSoc Agents & LLMs: Agentic AI for Scientific and Societal Advances (opens in a new tab)
- ECML-PKDDWhen GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series (opens in a new tab)
202518 papers
- ICLR (T1)A Large-scale Training Paradigm for Graph Generative Models (opens in a new tab)
- Findings of NAACLDemystifying the Power of Large Language Models in Graph Generation (opens in a new tab)
- CIKM (T1)GraFS: An Integrated GNN-LLM Approach for Inferring Best Functional Substitute Products (opens in a new tab)
- AISTATS (T1)Learning Laplacian Positional Encodings for Heterophilous Graphs (opens in a new tab)
- CIKM (T1)LinkGPT: Leveraging Large Language Models for Enhanced Link Prediction in Text-Attributed Graphs (opens in a new tab)
- AAMAS (T1)MAGNET: A Multi-Agent Graph Neural Network for Efficient Bipartite Task Assignment (opens in a new tab)
- CVPR (T1)Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning (opens in a new tab)
- GRADES/NDANot all Neighbors Agree: Graph Learning Beyond Homophily (opens in a new tab)
- KDD (T1)On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective (opens in a new tab)
- NeurIPS (T1)Random Search Neural Networks for Efficient and Expressive Graph Learning (opens in a new tab)
- KDD (T1)SciSoc LLM Workshop: Large Language Models for Scientific and Societal Advances (opens in a new tab)
- ACM Trans. Knowl. Discov. DataSimplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation (opens in a new tab)
20249 papers
- ICLR (T1)Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks (opens in a new tab)
- ICML (T1)Editing Partially Observable Networks via Graph Diffusion Models (opens in a new tab)
- WWW (T1)Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training (opens in a new tab)
- Findings of EMNLPMulti-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (opens in a new tab)
- ICASSP (T1)On Estimating Link Prediction Uncertainty Using Stochastic Centering (opens in a new tab)
- NeurIPS (T1)On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks (opens in a new tab)
- WSDM (T1)Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices (opens in a new tab)
- SIGIR (T1)TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning (opens in a new tab)
- TKDE (T1)Unified Dense Subgraph Detection: Fast Spectral Theory Based Algorithms (opens in a new tab)
20238 papers
- ICLR (T1)A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias (opens in a new tab)
- ICASSP (T1)A Closer Look At Scoring Functions And Generalization Prediction (opens in a new tab)
- AAAI (T1)A Provable Framework of Learning Graph Embeddings via Summarization (opens in a new tab)
- IEEE Data Eng. Bull.Heterophily and Graph Neural Networks: Past, Present and Future (opens in a new tab)
- KDD (T1)Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks (opens in a new tab)
- LoGOn Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks (opens in a new tab)
- Dagstuhl ReportsScalable Graph Mining and Learning (Dagstuhl Seminar 23491) (opens in a new tab)
- KDD (T1)The 3rd Workshop on Graph Learning Benchmarks (GLB 2023) (opens in a new tab)
Where alumni went46 listed
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
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