Danai Koutra
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
84
Venues
22
Active years
2012–2026
Best venue rank
A*
Where they publish
- A*KDD16 papers
- A*WWW9 papers
- A*ICDM9 papers
- ASDM8 papers
- ACIKM7 papers
- A*EMNLP5 papers
- BPAKDD5 papers
- A*AAAI3 papers
- A*ICLR3 papers
- AWSDM3 papers
- A*SIGIR2 papers
- AAISTATS2 papers
- MulticonferenceICASSP2 papers
- A*SIGMOD2 papers
- A*CVPR1 paper
- ANAACL1 paper
- A*ICML1 paper
- BDSAA1 paper
- BEDBT1 paper
- BSSDBM1 paper
- ACSCW1 paper
- A*UIST1 paper
Papers
84 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | GraphTextack: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs. | Jiaji Ma, Puja Trivedi, Danai Koutra |
| 2026 | WWW | AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents. | Mingdai Yang, Nurendra Choudhary, Jiangshu Du, Edward W. Huang, Philip S. Yu, Karthik Subbian, Danai Koutra |
| 2026 | SIGIR | Beyond Unimodal Perspectives: Generative Retrieval with Multimodal Semantics. | Jing Zhu, Mingxuan Ju, Yozen Liu, Shubham Vij, Danai Koutra, Neil Shah, Tong Zhao |
| 2025 | AISTATS | Understanding GNNs and Homophily in Dynamic Node Classification. | Michael Ito, Danai Koutra, Jenna Wiens |
| 2025 | AISTATS | Learning Laplacian Positional Encodings for Heterophilous Graphs. | Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens |
| 2025 | CIKM | LinkGPT: Leveraging Large Language Models for Enhanced Link Prediction in Text-Attributed Graphs. | Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Chai, Danai Koutra |
| 2025 | CIKM | GraFS: An Integrated GNN-LLM Approach for Inferring Best Functional Substitute Products. | Favour Nerrise, Edward W. Huang, Xiaonan Ji, Karthik Subbian, Danai Koutra |
| 2025 | CVPR | Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning. | Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra |
| 2025 | ICLR | A Large-scale Training Paradigm for Graph Generative Models. | Yu Wang, Ryan A. Rossi, Namyong Park, Huiyuan Chen, Nesreen K. Ahmed, Puja Trivedi, Franck Dernoncourt, Danai Koutra, Tyler Derr |
| 2025 | KDD | SKnow-LLM Workshop: Structured Knowledge for Large Language Models. | Qi Zhu, Xiusi Chen, Yu Zhang, Soji Adeshina, Costas Mavromatis, Zhen Han, Vassilis N. Ioannidis, Leman Akoglu, Danai Koutra, Huzefa Rangwala |
| 2025 | KDD | SciSoc LLM Workshop: Large Language Models for Scientific and Societal Advances. | Wei Jin, Lu Cheng, Wenpeng Yin, Xianfeng Tang, Qingsong Wen, Danai Koutra, B. Aditya Prakash, Yan Liu |
| 2025 | KDD | Tackling Size Generalization of Graph Neural Networks on Biological Data from a Spectral Perspective. | Gaotang Li, Danai Koutra, Yujun Yan |
| 2025 | KDD | On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective. | Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Danai Koutra |
| 2025 | NAACL | Demystifying the Power of Large Language Models in Graph Generation. | Yu Wang, Ryan A. Rossi, Namyong Park, Nesreen K. Ahmed, Danai Koutra, Franck Dernoncourt, Tyler Derr |
| 2025 | WWW | Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation. | Lifu Huang, Danai Koutra, Adithya Kulkarni, Temiloluwa Prioleau, Qingyun Wu, Yujun Yan, Yaoqing Yang, James Zou, Dawei Zhou |
| 2025 | WWW | Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. | Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju |
| 2025 | SDM | Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks. | Donald Loveland, Danai Koutra |
| 2024 | EMNLP | Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation. | Yuhang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang |
| 2024 | ICASSP | On Estimating Link Prediction Uncertainty Using Stochastic Centering. | Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan |
| 2024 | ICLR | Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. | Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan |
| 2024 | ICML | Editing Partially Observable Networks via Graph Diffusion Models. | Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra |
| 2024 | WWW | Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training. | Charles Dickens, Edward W. Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra |
| 2024 | SIGIR | TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. | Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos |
| 2024 | WSDM | Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices. | Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra |
| 2023 | AAAI | A Provable Framework of Learning Graph Embeddings via Summarization. | Houquan Zhou, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng |
| 2023 | ICASSP | A Closer Look At Scoring Functions And Generalization Prediction. | Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan |
| 2023 | ICLR | A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. | Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan |
| 2023 | KDD | The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). | Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka Zitnik |
| 2023 | KDD | Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks. | Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan |
| 2022 | CIKM | CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. | Jing Zhu, Danai Koutra, Mark Heimann |
| 2022 | CIKM | Leveraging the Graph Structure of Neural Network Training Dynamics. | Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra |
| 2022 | ICDM | Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. | Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra |
| 2022 | KDD | How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. | Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra |
| 2022 | WWW | Accepted Tutorials at The Web Conference 2022. | Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lyu, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian M. Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro A. Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis-Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He |
| 2022 | WWW | Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. | Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra |
| 2022 | WSDM | On Generalizing Static Node Embedding to Dynamic Settings. | Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra |
| 2021 | AAAI | Graph Neural Networks with Heterophily. | Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra |
| 2021 | EMNLP | Relational World Knowledge Representation in Contextual Language Models: A Review. | Tara Safavi, Danai Koutra |
| 2021 | EMNLP | NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases. | Tara Safavi, Jing Zhu, Danai Koutra |
| 2021 | SDM | Refining Network Alignment to Improve Matched Neighborhood Consistency. | Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra |
| 2021 | SDM | Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. | Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra |
| 2020 | CIKM | CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. | Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra |
| 2020 | CIKM | G-CREWE: Graph CompREssion With Embedding for Network Alignment. | Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra |
| 2020 | DSAA | Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. | Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra |
| 2020 | EMNLP | CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. | Tara Safavi, Danai Koutra |
| 2020 | EMNLP | Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction. | Tara Safavi, Danai Koutra, Edgar Meij |
| 2020 | ICDM | A Hidden Challenge of Link Prediction: Which Pairs to Check? | Caleb Belth, Alican Bykakir, Danai Koutra |
| 2020 | KDD | Mining Persistent Activity in Continually Evolving Networks. | Caleb Belth, Xinyi Zheng, Danai Koutra |
| 2020 | WWW | What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. | Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra |
| 2020 | WSDM | Toward Activity Discovery in the Personal Web. | Tara Safavi, Adam Fourney, Robert Sim, Marcin Juraszek, Shane Williams, Ned Friend, Danai Koutra, Paul N. Bennett |
| 2019 | ICDM | Distribution of Node Embeddings as Multiresolution Features for Graphs. | Mark Heimann, Tara Safavi, Danai Koutra |
| 2019 | ICDM | Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket. | Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Mller, Danai Koutra |
| 2019 | KDD | Smart Roles: Inferring Professional Roles in Email Networks. | Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, Danai Koutra |
| 2019 | KDD | Latent Network Summarization: Bridging Network Embedding and Summarization. | Di Jin, Ryan A. Rossi, Eunyee Koh, Sungchul Kim, Anup Rao, Danai Koutra |
| 2019 | KDD | GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data. | Yujun Yan, Jiong Zhu, Marlena Duda, Eric Solarz, Chandra Sekhar Sripada, Danai Koutra |
| 2019 | SDM | Coupled Clustering of Time-Series and Networks. | Yike Liu, Linhong Zhu, Pedro A. Szekely, Aram Galstyan, Danai Koutra |
| 2018 | CIKM | REGAL: Representation Learning-based Graph Alignment. | Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra |
| 2018 | EDBT | GeoAlign: Interpolating Aggregates over Unaligned Partitions. | Jie Song, Danai Koutra, Murali Mani, H. V. Jagadish |
| 2018 | ICDM | Summarizing Graphs at Multiple Scales: New Trends. | Danai Koutra, Jilles Vreeken, Francesco Bonchi |
| 2018 | KDD | Career Transitions and Trajectories: A Case Study in Computing. | Tara Safavi, Maryam Davoodi, Danai Koutra |
| 2018 | PAKDD | HashAlign: Hash-Based Alignment of Multiple Graphs. | Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen, Danai Koutra |
| 2018 | SIGMOD | GeoFlux: Hands-Off Data Integration Leveraging Join Key Knowledge. | Jie Song, Danai Koutra, Murali Mani, H. V. Jagadish |
| 2018 | SDM | Fast Flow-based Random Walk with Restart in a Multi-query Setting. | Yujun Yan, Mark Heimann, Di Jin, Danai Koutra |
| 2017 | ICDM | Exploratory Analysis of Graph Data by Leveraging Domain Knowledge. | Di Jin, Danai Koutra |
| 2017 | ICDM | Inferring, Summarizing and Mining Multi-source Graph Data. | Danai Koutra |
| 2017 | ICDM | Scalable Hashing-Based Network Discovery. | Tara Safavi, Chandra Sekhar Sripada, Danai Koutra |
| 2017 | KDD | PNP: Fast Path Ensemble Method for Movie Design. | Danai Koutra, Abhilash Dighe, Smriti Bhagat, Udi Weinsberg, Stratis Ioannidis, Christos Faloutsos, Jean Bolot |
| 2017 | SSDBM | Edge Labeling Schemes for Graph Data. | Oshini Goonetilleke, Danai Koutra, Timos Sellis, Kewen Liao |
| 2016 | CSCW | Coding Varied Behavior Types Using the Crowd. | Jinyeong Yim, Jeel Jasani, Aubrey Henderson, Danai Koutra, Steven Dow, Winnie Leung, Ellen Lim, Mitchell L. Gordon, Jeffrey P. Bigham, Walter S. Lasecki |
| 2016 | SDM | On Skewed Multi-dimensional Distributions: the FusionRP Model, Algorithms, and Discoveries. | Venkata Krishna Pillutla, Zhanpeng Fang, Pravallika Devineni, Christos Faloutsos, Danai Koutra, Jie Tang |
| 2015 | KDD | TimeCrunch: Interpretable Dynamic Graph Summarization. | Neil Shah, Danai Koutra, Tianmin Zou, Brian Gallagher, Christos Faloutsos |
| 2015 | WWW | Events and Controversies: Influences of a Shocking News Event on Information Seeking. | Danai Koutra, Paul N. Bennett, Eric Horvitz |
| 2014 | PAKDD | Com2: Fast Automatic Discovery of Temporal ('Comet') Communities. | Miguel Araujo, Spiros Papadimitriou, Stephan Gnnemann, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra |
| 2014 | PAKDD | Net-Ray: Visualizing and Mining Billion-Scale Graphs. | U Kang, Jay Yoon Lee, Danai Koutra, Christos Faloutsos |
| 2014 | PAKDD | Influence Propagation: Patterns, Model and a Case Study. | Yibin Lin, Agha Ali Raza, Jay Yoon Lee, Danai Koutra, Roni Rosenfeld, Christos Faloutsos |
| 2014 | UIST | Glance: rapidly coding behavioral video with the crowd. | Walter S. Lasecki, Mitchell L. Gordon, Danai Koutra, Malte F. Jung, Steven P. Dow, Jeffrey P. Bigham |
| 2014 | SDM | VOG: Summarizing and Understanding Large Graphs. | Danai Koutra, U Kang, Jilles Vreeken, Christos Faloutsos |
| 2013 | ICDM | BIG-ALIGN: Fast Bipartite Graph Alignment. | Danai Koutra, Hanghang Tong, David M. Lubensky |
| 2013 | KDD | Detecting insider threats in a real corporate database of computer usage activity. | Ted E. Senator, Henry G. Goldberg, Alex Memory, William T. Young, Brad Rees, Robert Pierce, Daniel Huang, Matthew Reardon, David A. Bader, Edmond Chow, Irfan A. Essa, Joshua Jones, Vinay Bettadapura, Duen Horng Chau, Oded Green, Oguz Kaya, Anita Zakrzewska, Erica Briscoe, Rudolph L. Mappus IV, Robert McColl, Lora Weiss, Thomas G. Dietterich, Alan Fern, Weng-Keen Wong, Shubhomoy Das, Andrew Emmott, Jed Irvine, Jay Yoon Lee, Danai Koutra, Christos Faloutsos, Daniel D. Corkill, Lisa Friedland, Amanda Gentzel, David D. Jensen |
| 2013 | PAKDD | Patterns amongst Competing Task Frequencies: Super-Linearities, and the Almond-DG Model. | Danai Koutra, Vasileios Koutras, B. Aditya Prakash, Christos Faloutsos |
| 2013 | WWW | Fast anomaly detection despite the duplicates. | Jay Yoon Lee, U Kang, Danai Koutra, Christos Faloutsos |
| 2013 | SDM | DELTACON: A Principled Massive-Graph Similarity Function. | Christos Faloutsos, Danai Koutra, Joshua T. Vogelstein |
| 2012 | KDD | RolX: structural role extraction & mining in large graphs. | Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, Lei Li |
| 2012 | SIGMOD | OPAvion: mining and visualization in large graphs. | Leman Akoglu, Duen Horng Chau, U Kang, Danai Koutra, Christos Faloutsos |