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2020 – today
- 2023
[i48]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. CoRR abs/2303.13500 (2023)- [i47]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Scoring Functions and Generalization Prediction. CoRR abs/2303.13589 (2023) - [i46]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) - 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, Ben 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 - [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]