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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) - [c85]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. EMNLP (Findings) 2024: 3315-3333 - [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 - [i66]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) - [i65]Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Chai, Danai Koutra:
LinkGPT: Teaching Large Language Models To Predict Missing Links. CoRR abs/2406.04640 (2024) - [i64]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) - [i63]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) - [i62]Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra:
Multimodal Graph Benchmark. CoRR abs/2406.16321 (2024) - [i61]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) - [i60]Donald Loveland, Danai Koutra:
Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks. CoRR abs/2410.04287 (2024) - [i59]Jiacheng Lin, Kun Qian, Haoyu Han, Nurendra Choudhary, Tianxin Wei, Zhongruo Wang, Sahika Genc, Edward W. Huang, Sheng Wang, Karthik Subbian, Danai Koutra, Jimeng Sun:
Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data. CoRR abs/2410.11235 (2024) - [i58]Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju:
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. CoRR abs/2410.23300 (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]