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2020 – today
- 2024
[j27]Wenjie Feng
, Shenghua Liu, Danai Koutra, Xueqi Cheng:
Unified Dense Subgraph Detection: Fast Spectral Theory Based Algorithms. IEEE Trans. Knowl. Data Eng. 36(3): 1356-1370 (2024)- [c84]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
On Estimating Link Prediction Uncertainty Using Stochastic Centering. ICASSP 2024: 6810-6814 - [c83]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR 2024 - [c82]Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra:
Editing Partially Observable Networks via Graph Diffusion Models. ICML 2024 - [c81]Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos:
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. SIGIR 2024: 2662-2666 - [c80]Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra:
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices. WSDM 2024: 994-1002 - [c79]Charles Dickens, Edward W. Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra:
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training. WWW (Companion Volume) 2024: 1502-1510 - [i63]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR abs/2401.03350 (2024) - [i62]Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Chai, Danai Koutra:
LinkGPT: Teaching Large Language Models To Predict Missing Links. CoRR abs/2406.04640 (2024) - [i61]Yu Wang, Ryan A. Rossi, Namyong Park, Huiyuan Chen, Nesreen K. Ahmed, Puja Trivedi, Franck Dernoncourt, Danai Koutra, Tyler Derr:
Large Generative Graph Models. CoRR abs/2406.05109 (2024) - [i60]Yuhang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang:
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation. CoRR abs/2406.13114 (2024) - [i59]Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra:
Multimodal Graph Benchmark. CoRR abs/2406.16321 (2024) - [i58]Jiong Zhu, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, Danai Koutra:
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks. CoRR abs/2409.17475 (2024) - 2023
- [j26]Jiong Zhu, Yujun Yan, Mark Heimann, Lingxiao Zhao, Leman Akoglu, Danai Koutra:
Heterophily and Graph Neural Networks: Past, Present and Future. IEEE Data Eng. Bull. 46(2): 12-34 (2023) - [c78]Houquan Zhou, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng:
A Provable Framework of Learning Graph Embeddings via Summarization. AAAI 2023: 4946-4953 - [c77]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look At Scoring Functions And Generalization Prediction. ICASSP 2023: 1-5 - [c76]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. ICLR 2023 - [c75]Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan:
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks. KDD 2023: 1223-1234 - [c74]Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka Zitnik:
The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). KDD 2023: 5870-5871 - [c73]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG 2023: 6 - [e5]Danai Koutra, Claudia Plant, Manuel Gomez-Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part I. Lecture Notes in Computer Science 14169, Springer 2023, ISBN 978-3-031-43411-2 [contents] - [e4]Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part II. Lecture Notes in Computer Science 14170, Springer 2023, ISBN 978-3-031-43414-3 [contents] - [e3]Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part III. Lecture Notes in Computer Science 14171, Springer 2023, ISBN 978-3-031-43417-4 [contents] - [e2]Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part IV. Lecture Notes in Computer Science 14172, Springer 2023, ISBN 978-3-031-43420-4 [contents] - [e1]Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part V. Lecture Notes in Computer Science 14173, Springer 2023, ISBN 978-3-031-43423-5 [contents] - [i57]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. CoRR abs/2303.13500 (2023) - [i56]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Scoring Functions and Generalization Prediction. CoRR abs/2303.13589 (2023) - [i55]Jiong Zhu, Aishwarya Reganti, Edward W. Huang, Charles Dickens, Nikhil Rao, Karthik Subbian, Danai Koutra:
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation. CoRR abs/2305.09887 (2023) - [i54]Yujun Yan, Gaotang Li, Danai Koutra:
Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective. CoRR abs/2305.15611 (2023) - [i53]Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra:
SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks. CoRR abs/2306.00899 (2023) - [i52]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. CoRR abs/2306.05557 (2023) - [i51]Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan:
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks. CoRR abs/2306.14375 (2023) - [i50]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR abs/2309.10976 (2023) - [i49]Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos:
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. CoRR abs/2309.13885 (2023) - [i48]Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra:
Leveraging Graph Diffusion Models for Network Refinement Tasks. CoRR abs/2311.17856 (2023) - [i47]Charles Dickens, Eddie W. Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra:
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training. CoRR abs/2312.15520 (2023) - [i46]Danai Koutra, Henning Meyerhenke, Ilya Safro, Fabian Brandt-Tumescheit:
Scalable Graph Mining and Learning (Dagstuhl Seminar 23491). Dagstuhl Reports 13(12): 1-23 (2023) - 2022
- [j25]Caleb Belth, Alican Büyükçakir, Danai Koutra:
A hidden challenge of link prediction: which pairs to check? Knowl. Inf. Syst. 64(3): 743-771 (2022) - [j24]Junchen Jin, Mark Heimann, Di Jin, Danai Koutra:
Toward Understanding and Evaluating Structural Node Embeddings. ACM Trans. Knowl. Discov. Data 16(3): 58:1-58:32 (2022) - [c72]Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra:
Leveraging the Graph Structure of Neural Network Training Dynamics. CIKM 2022: 4545-4549 - [c71]Jing Zhu, Danai Koutra, Mark Heimann:
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CIKM 2022: 4747-4751 - [c70]Ekdeep Singh Lubana, Puja Trivedi, Danai Koutra, Robert P. Dick:
How do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation. CoLLAs 2022: 819-837 - [c69]Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra:
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. ICDM 2022: 1287-1292 - [c68]Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra:
How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. KDD 2022: 2637-2647 - [c67]Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan:
Analyzing Data-Centric Properties for Graph Contrastive Learning. NeurIPS 2022 - [c66]Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra:
On Generalizing Static Node Embedding to Dynamic Settings. WSDM 2022: 410-420 - [c65]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 Lye, 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:
Accepted Tutorials at The Web Conference 2022. WWW (Companion Volume) 2022: 391-399 - [c64]Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra:
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. WWW 2022: 1538-1549 - [i45]Houquan Zhou, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng:
Learning node embeddings via summary graphs: a brief theoretical analysis. CoRR abs/2207.01189 (2022) - [i44]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR abs/2207.04376 (2022) - [i43]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety. CoRR abs/2207.12615 (2022) - [i42]Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan:
Analyzing Data-Centric Properties for Contrastive Learning on Graphs. CoRR abs/2208.02810 (2022) - [i41]Jing Zhu, Danai Koutra, Mark Heimann:
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CoRR abs/2208.10682 (2022) - 2021
- [j23]Danai Koutra:
The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability. Proc. VLDB Endow. 14(13): 3416 (2021) - [j22]Di Jin, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Danai Koutra:
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation. Proc. VLDB Endow. 15(3): 465-477 (2021) - [c63]Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra:
Graph Neural Networks with Heterophily. AAAI 2021: 11168-11176 - [c62]Tara Safavi, Danai Koutra:
Relational World Knowledge Representation in Contextual Language Models: A Review. EMNLP (1) 2021: 1053-1067 - [c61]Tara Safavi, Jing Zhu, Danai Koutra:
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases. EMNLP (1) 2021: 5633-5646 - [c60]Nishil Talati, Di Jin, Haojie Ye, Ajay Brahmakshatriya, Ganesh S. Dasika, Saman P. Amarasinghe, Trevor N. Mudge, Danai Koutra, Ronald G. Dreslinski:
A Deep Dive Into Understanding The Random Walk-Based Temporal Graph Learning. IISWC 2021: 87-100 - [c59]Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra:
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. SDM 2021: 163-171 - [c58]Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra:
Refining Network Alignment to Improve Matched Neighborhood Consistency. SDM 2021: 172-180 - [i40]Junchen Jin, Mark Heimann, Di Jin, Danai Koutra:
Towards Understanding and Evaluating Structural Node Embeddings. CoRR abs/2101.05730 (2021) - [i39]Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra:
Refining Network Alignment to Improve Matched Neighborhood Consistency. CoRR abs/2101.08808 (2021) - [i38]Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra:
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. CoRR abs/2102.06462 (2021) - [i37]Caleb Belth, Alican Büyükçakir, Danai Koutra:
A Hidden Challenge of Link Prediction: Which Pairs to Check? CoRR abs/2102.07878 (2021) - [i36]Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra:
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. CoRR abs/2102.13582 (2021) - [i35]Tara Safavi, Danai Koutra:
Relational world knowledge representation in contextual language models: A review. CoRR abs/2104.05837 (2021) - [i34]Jiong Zhu, Junchen Jin, Michael T. Schaub, Danai Koutra:
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs. CoRR abs/2106.07767 (2021) - [i33]Di Jin, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Danai Koutra:
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation. CoRR abs/2110.14509 (2021) - [i32]Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra:
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. CoRR abs/2111.03220 (2021) - [i31]Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra:
Convolutional Neural Network Dynamics: A Graph Perspective. CoRR abs/2111.05410 (2021) - 2020
- [j21]Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, Jenna Wiens:
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Medical Informatics Assoc. 27(12): 1921-1934 (2020) - [j20]Saba A. Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam:
t-PINE: tensor-based predictable and interpretable node embeddings. Soc. Netw. Anal. Min. 10(1): 46 (2020) - [j19]Ryan A. Rossi, Di Jin, Sungchul Kim, Nesreen K. Ahmed, Danai Koutra, John Boaz Lee:
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications. ACM Trans. Knowl. Discov. Data 14(5): 63:1-63:37 (2020) - [c57]Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra:
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CIKM 2020: 1255-1264 - [c56]Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra:
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. CIKM 2020: 1985-1988 - [c55]Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra:
Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. DSAA 2020: 380-389 - [c54]Tara Safavi, Danai Koutra, Edgar Meij:
Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction. EMNLP (1) 2020: 8308-8321 - [c53]Tara Safavi, Danai Koutra:
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. EMNLP (1) 2020: 8328-8350 - [c52]Caleb Belth, Alican Büyükçakir, Danai Koutra:
A Hidden Challenge of Link Prediction: Which Pairs to Check? ICDM 2020: 831-840 - [c51]Scott McMillan, Manoj Kumar, Danai Koutra, Mahantesh Halappanavar, Tim Mattson, Antonino Tumeo:
Message from the workshop chairs. IPDPS Workshops 2020: 199-200 - [c50]Caleb Belth, Xinyi Zheng, Danai Koutra:
Mining Persistent Activity in Continually Evolving Networks. KDD 2020: 934-944 - [c49]Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi:
Neural Execution Engines: Learning to Execute Subroutines. NeurIPS 2020 - [c48]Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra:
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. NeurIPS 2020 - [c47]Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng:
SpecGreedy: Unified Dense Subgraph Detection. ECML/PKDD (1) 2020: 181-197 - [c46]Tara Safavi, Adam Fourney, Robert Sim, Marcin Juraszek, Shane Williams, Ned Friend, Danai Koutra, Paul N. Bennett:
Toward Activity Discovery in the Personal Web. WSDM 2020: 492-500 - [c45]Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra:
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. WWW 2020: 1115-1126 - [i30]Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra:
Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data. CoRR abs/2002.10010 (2020) - [i29]Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra:
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. CoRR abs/2003.10412 (2020) - [i28]Tara Safavi, Danai Koutra, Edgar Meij:
Improving the Utility of Knowledge Graph Embeddings with Calibration. CoRR abs/2004.01168 (2020) - [i27]Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra:
Consistent Network Alignment with Node Embedding. CoRR abs/2005.04725 (2020) - [i26]Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi:
Neural Execution Engines: Learning to Execute Subroutines. CoRR abs/2006.08084 (2020) - [i25]Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra:
Generalizing Graph Neural Networks Beyond Homophily. CoRR abs/2006.11468 (2020) - [i24]Caleb Belth, Xinyi Zheng, Danai Koutra:
Mining Persistent Activity in Continually Evolving Networks. CoRR abs/2006.15410 (2020) - [i23]Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra:
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CoRR abs/2007.16208 (2020) - [i22]Tara Safavi, Danai Koutra:
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. CoRR abs/2009.07810 (2020) - [i21]Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra:
From Static to Dynamic Node Embeddings. CoRR abs/2009.10017 (2020) - [i20]Jiong Zhu, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra:
Graph Neural Networks with Heterophily. CoRR abs/2009.13566 (2020) - [i19]Tara Safavi, Danai Koutra:
Generating Negative Commonsense Knowledge. CoRR abs/2011.07497 (2020)
2010 – 2019
- 2019
- [j18]Saba A. Al-Sayouri, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam:
SURREAL: Subgraph Robust Representation Learning. Appl. Netw. Sci. 4(1): 88:1-88:20 (2019) - [j17]Oshini Goonetilleke, Danai Koutra, Kewen Liao, Timos Sellis:
On effective and efficient graph edge labeling. Distributed Parallel Databases 37(1): 5-38 (2019) - [j16]Tara Safavi, Chandra Sekhar Sripada, Danai Koutra:
Fast network discovery on sequence data via time-aware hashing. Knowl. Inf. Syst. 61(2): 987-1017 (2019) - [j15]Asso Hamzehei, Raymond K. Wong, Danai Koutra, Fang Chen:
Collaborative topic regression for predicting topic-based social influence. Mach. Learn. 108(10): 1831-1850 (2019) - [j14]Pin-Yu Chen, Chun-Chen Tu, Pai-Shun Ting, Ya-Yun Lo, Danai Koutra, Alfred O. Hero III:
Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach. IEEE Trans. Signal Inf. Process. over Networks 5(1): 139-151 (2019) - [c44]Caleb Belth, Fahad Kamran, Donna Tjandra, Danai Koutra:
When to remember where you came from: node representation learning in higher-order networks. ASONAM 2019: 222-225 - [c43]Sang Won Lee, Aaron Willette, Danai Koutra, Walter S. Lasecki:
The Effect of Social Interaction on Facilitating Audience Participation in a Live Music Performance. Creativity & Cognition 2019: 108-120 - [c42]Mark Heimann, Tara Safavi, Danai Koutra:
Distribution of Node Embeddings as Multiresolution Features for Graphs. ICDM 2019: 289-298 - [c41]Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Müller, Danai Koutra:
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket. ICDM 2019: 528-537 - [c40]Yujun Yan, Jiong Zhu, Marlena Duda, Eric Solarz, Chandra Sekhar Sripada, Danai Koutra:
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data. KDD 2019: 772-782 - [c39]Di Jin, Ryan A. Rossi, Eunyee Koh, Sungchul Kim, Anup Rao, Danai Koutra:
Latent Network Summarization: Bridging Network Embedding and Summarization. KDD 2019: 987-997 - [c38]Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, Danai Koutra:
Smart Roles: Inferring Professional Roles in Email Networks. KDD 2019: 2923-2933 - [c37]Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra:
node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching. ECML/PKDD (1) 2019: 483-506 - [c36]Yike Liu, Linhong Zhu, Pedro A. Szekely, Aram Galstyan, Danai Koutra:
Coupled Clustering of Time-Series and Networks. SDM 2019: 531-539 - [i18]Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra:
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching. CoRR abs/1904.08572 (2019) - [i17]Ryan A. Rossi, Di Jin, Sungchul Kim, Nesreen K. Ahmed, Danai Koutra, John Boaz Lee:
From Community to Role-based Graph Embeddings. CoRR abs/1908.08572 (2019) - 2018
- [j13]Yike Liu, Tara Safavi, Abhilash Dighe, Danai Koutra:
Graph Summarization Methods and Applications: A Survey. ACM Comput. Surv. 51(3): 62:1-62:34 (2018) - [j12]Geoffrey D. Hannigan, Melissa Beth Duhaime, Danai Koutra, Patrick D. Schloss:
Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome. PLoS Comput. Biol. 14(4) (2018) - [j11]Yike Liu, Tara Safavi, Neil Shah, Danai Koutra:
Reducing large graphs to small supergraphs: a unified approach. Soc. Netw. Anal. Min. 8(1): 17 (2018) - [c35]Saba A. Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam:
t-PNE: Tensor-Based Predictable Node Embeddings. ASONAM 2018: 491-494 - [c34]Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra:
REGAL: Representation Learning-based Graph Alignment. CIKM 2018: 117-126 - [c33]Jie Song, Danai Koutra, Murali Mani, H. V. Jagadish:
GeoAlign: Interpolating Aggregates over Unaligned Partitions. EDBT 2018: 361-372 - [c32]Danai Koutra, Jilles Vreeken, Francesco Bonchi:
Summarizing Graphs at Multiple Scales: New Trends. ICDM 2018: 1097 - [c31]Tara Safavi, Maryam Davoodi, Danai Koutra:
Career Transitions and Trajectories: A Case Study in Computing. KDD 2018: 675-684 - [c30]Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen, Danai Koutra:
HashAlign: Hash-Based Alignment of Multiple Graphs. PAKDD (3) 2018: 726-739 - [c29]Yujun Yan, Mark Heimann, Di Jin, Danai Koutra:
Fast Flow-based Random Walk with Restart in a Multi-query Setting. SDM 2018: 342-350 - [c28]Jie Song, Danai Koutra, Murali Mani, H. V. Jagadish:
GeoFlux: Hands-Off Data Integration Leveraging Join Key Knowledge. SIGMOD Conference 2018: 1797-1800 - [i16]Mark Heimann, Haoming Shen, Danai Koutra:
Node Representation Learning for Multiple Networks: The Case of Graph Alignment. CoRR abs/1802.06257 (2018) - [i15]Saba A. Al-Sayouri, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam:
SURREAL: SUbgraph Robust REpresentAtion Learning. CoRR abs/1805.01509 (2018) - [i14]Saba A. Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam:
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings. CoRR abs/1805.01889 (2018) - [i13]Tara Safavi, Maryam Davoodi, Danai Koutra:
Career Transitions and Trajectories: A Case Study in Computing. CoRR abs/1805.06534 (2018) - [i12]Di Jin, Ryan A. Rossi, Danai Koutra, Eunyee Koh, Sungchul Kim, Anup Rao:
Bridging Network Embedding and Graph Summarization. CoRR abs/1811.04461 (2018) - 2017
- [b1]Danai Koutra, Christos Faloutsos:
Individual and Collective Graph Mining: Principles, Algorithms, and Applications. Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool Publishers 2017, ISBN 978-3-031-00783-5 - [j10]Asso Hamzehei, Shanqing Jiang, Danai Koutra, Raymond K. Wong, Fang Chen:
Topic-based Social Influence Measurement for Social Networks. Australas. J. Inf. Syst. 21 (2017) - [j9]Neil Shah, Danai Koutra, Lisa Jin, Tianmin Zou, Brian Gallagher, Christos Faloutsos:
On Summarizing Large-Scale Dynamic Graphs. IEEE Data Eng. Bull. 40(3): 75-88 (2017) - [j8]Di Jin, Aristotelis Leventidis, Haoming Shen, Ruowang Zhang, Junyue Wu, Danai Koutra:
PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs. Informatics 4(3): 22 (2017) - [j7]Pravallika Devineni, Danai Koutra, Michalis Faloutsos, Christos Faloutsos:
Facebook wall posts: a model of user behaviors. Soc. Netw. Anal. Min. 7(1): 6:1-6:15 (2017) - [c27]Pravallika Devineni, Evangelos E. Papalexakis, Danai Koutra, A. Seza Dogruöz, Michalis Faloutsos:
One Size Does Not Fit All: Profiling Personalized Time-Evolving User Behaviors. ASONAM 2017: 331-340 - [c26]Amanda J. Minnich, Nikan Chavoshi, Danai Koutra, Abdullah Mueen:
BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks. ASONAM 2017: 467-474 - [c25]Di Jin, Danai Koutra:
Exploratory Analysis of Graph Data by Leveraging Domain Knowledge. ICDM 2017: 187-196 - [c24]Tara Safavi, Chandra Sekhar Sripada, Danai Koutra:
Scalable Hashing-Based Network Discovery. ICDM 2017: 405-414 - [c23]Danai Koutra:
Inferring, Summarizing and Mining Multi-source Graph Data. ICDM Workshops 2017: 978 - [c22]Danai Koutra, Abhilash Dighe, Smriti Bhagat, Udi Weinsberg, Stratis Ioannidis, Christos Faloutsos, Jean Bolot:
PNP: Fast Path Ensemble Method for Movie Design. KDD 2017: 1527-1536 - [c21]Oshini Goonetilleke, Danai Koutra, Timos Sellis, Kewen Liao:
Edge Labeling Schemes for Graph Data. SSDBM 2017: 12:1-12:12 - [i11]Josh Gardner, Danai Koutra, Jawad Mroueh, Victor Pang, Arya Farahi, Sam Krassenstein, Jared Webb:
Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit. CoRR abs/1710.06839 (2017) - 2016
- [j6]Miguel Araujo, Stephan Günnemann, Spiros Papadimitriou, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra:
Discovery of "comet" communities in temporal and labeled graphs Com^2. Knowl. Inf. Syst. 46(3): 657-677 (2016) - [j5]Danai Koutra, Neil Shah, Joshua T. Vogelstein, Brian Gallagher, Christos Faloutsos:
DeltaCon: Principled Massive-Graph Similarity Function with Attribution. ACM Trans. Knowl. Discov. Data 10(3): 28:1-28:43 (2016) - [c20]Jinyeong Yim, Jeel Jasani, Aubrey Henderson, Danai Koutra, Steven Dow, Winnie Leung, Ellen Lim, Mitchell L. Gordon, Jeffrey P. Bigham, Walter S. Lasecki:
Coding Varied Behavior Types Using the Crowd. CSCW Companion 2016: 114-117 - [c19]Venkata Krishna Pillutla, Zhanpeng Fang, Pravallika Devineni, Christos Faloutsos, Danai Koutra, Jie Tang:
On Skewed Multi-dimensional Distributions: the FusionRP Model, Algorithms, and Discoveries. SDM 2016: 783-791 - [i10]Pin-Yu Chen, Chun-Chen Tu, Pai-Shun Ting, Ya-Yun Lo, Danai Koutra, Alfred O. Hero III:
Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach. CoRR abs/1609.05378 (2016) - [i9]Danai Koutra, Abhilash Dighe, Smriti Bhagat, Udi Weinsberg, Stratis Ioannidis, Christos Faloutsos, Jean Bolot:
PNP: Fast Path Ensemble Method for Movie Design. CoRR abs/1611.02388 (2016) - [i8]Yike Liu, Abhilash Dighe, Tara Safavi, Danai Koutra:
A Graph Summarization: A Survey. CoRR abs/1612.04883 (2016) - 2015
- [j4]Leman Akoglu, Hanghang Tong, Danai Koutra:
Graph based anomaly detection and description: a survey. Data Min. Knowl. Discov. 29(3): 626-688 (2015) - [j3]Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos:
Linearized and Single-Pass Belief Propagation. Proc. VLDB Endow. 8(5): 581-592 (2015) - [j2]Danai Koutra, Di Jin, Yuanshi Ning, Christos Faloutsos:
Perseus: An Interactive Large-Scale Graph Mining and Visualization Tool. Proc. VLDB Endow. 8(12): 1924-1927 (2015) - [j1]Danai Koutra, U Kang, Jilles Vreeken, Christos Faloutsos:
Summarizing and understanding large graphs. Stat. Anal. Data Min. 8(3): 183-202 (2015) - [c18]Pravallika Devineni, Danai Koutra, Michalis Faloutsos, Christos Faloutsos:
If walls could talk: Patterns and anomalies in Facebook wallposts. ASONAM 2015: 367-374 - [c17]Neil Shah, Danai Koutra, Tianmin Zou, Brian Gallagher, Christos Faloutsos:
TimeCrunch: Interpretable Dynamic Graph Summarization. KDD 2015: 1055-1064 - [c16]Danai Koutra, Paul N. Bennett, Eric Horvitz:
Events and Controversies: Influences of a Shocking News Event on Information Seeking. WWW 2015: 614-624 - [i7]Yike Liu, Neil Shah, Danai Koutra:
An Empirical Comparison of the Summarization Power of Graph Clustering Methods. CoRR abs/1511.06820 (2015) - 2014
- [c15]Miguel Araujo, Spiros Papadimitriou, Stephan Günnemann, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra:
Com2: Fast Automatic Discovery of Temporal ('Comet') Communities. PAKDD (2) 2014: 271-283 - [c14]U Kang, Jay Yoon Lee, Danai Koutra, Christos Faloutsos:
Net-Ray: Visualizing and Mining Billion-Scale Graphs. PAKDD (1) 2014: 348-361 - [c13]Yibin Lin, Agha Ali Raza, Jay Yoon Lee, Danai Koutra, Roni Rosenfeld, Christos Faloutsos:
Influence Propagation: Patterns, Model and a Case Study. PAKDD (1) 2014: 386-397 - [c12]Danai Koutra, U Kang, Jilles Vreeken, Christos Faloutsos:
VOG: Summarizing and Understanding Large Graphs. SDM 2014: 91-99 - [c11]Walter S. Lasecki, Mitchell L. Gordon, Danai Koutra, Malte F. Jung, Steven P. Dow, Jeffrey P. Bigham:
Glance: rapidly coding behavioral video with the crowd. UIST 2014: 551-562 - [i6]Leman Akoglu, Hanghang Tong, Danai Koutra:
Graph-based Anomaly Detection and Description: A Survey. CoRR abs/1404.4679 (2014) - [i5]Danai Koutra, Paul N. Bennett, Eric Horvitz:
Events and Controversies: Influences of a Shocking News Event on Information Seeking. CoRR abs/1405.1486 (2014) - [i4]Danai Koutra, U Kang, Jilles Vreeken, Christos Faloutsos:
VoG: Summarizing and Understanding Large Graphs. CoRR abs/1406.3411 (2014) - [i3]Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos:
Linearized and Turbo Belief Propagation. CoRR abs/1406.7288 (2014) - 2013
- [c10]Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, Christos Faloutsos:
Network similarity via multiple social theories. ASONAM 2013: 1439-1440 - [c9]Danai Koutra, Hanghang Tong, David M. Lubensky:
BIG-ALIGN: Fast Bipartite Graph Alignment. ICDM 2013: 389-398 - [c8]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:
Detecting insider threats in a real corporate database of computer usage activity. KDD 2013: 1393-1401 - [c7]Danai Koutra, Vasileios Koutras, B. Aditya Prakash, Christos Faloutsos:
Patterns amongst Competing Task Frequencies: Super-Linearities, and the Almond-DG Model. PAKDD (1) 2013: 201-212 - [c6]Christos Faloutsos, Danai Koutra, Joshua T. Vogelstein:
DELTACON: A Principled Massive-Graph Similarity Function. SDM 2013: 162-170 - [c5]Jay Yoon Lee, U Kang, Danai Koutra, Christos Faloutsos:
Fast anomaly detection despite the duplicates. WWW (Companion Volume) 2013: 195-196 - [i2]Danai Koutra, Joshua T. Vogelstein, Christos Faloutsos:
DELTACON: A Principled Massive-Graph Similarity Function. CoRR abs/1304.4657 (2013) - 2012
- [c4]Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, Lei Li:
RolX: structural role extraction & mining in large graphs. KDD 2012: 1231-1239 - [c3]Danai Koutra, Evangelos E. Papalexakis, Christos Faloutsos:
TensorSplat: Spotting Latent Anomalies in Time. Panhellenic Conference on Informatics 2012: 144-149 - [c2]Leman Akoglu, Duen Horng Chau, U Kang, Danai Koutra, Christos Faloutsos:
OPAvion: mining and visualization in large graphs. SIGMOD Conference 2012: 717-720 - [i1]Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, Christos Faloutsos:
NetSimile: A Scalable Approach to Size-Independent Network Similarity. CoRR abs/1209.2684 (2012) - 2011
- [c1]Danai Koutra, Tai-You Ke, U Kang, Duen Horng Chau, Hsing-Kuo Kenneth Pao, Christos Faloutsos:
Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms. ECML/PKDD (2) 2011: 245-260
Coauthor Index
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