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Publications (Google Scholar, DBLP)

Preprint

• Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!  
  Yuchen Shen, Haomin Wen, Leman Akoglu
  https://arxiv.org/abs/2409.05672, May 2025
  

• Unified Discrete Diffusion for Categorical Data
  Lingxiao Zhao, Xueying Ding, Leman Akoglu
  https://arxiv.org/abs/2402.03701, Jan. 2025
  

• Can Machine Learning Target Health Care Fraud? Evidence from Medicare Hospitalizations
  Shubhranshu Shekhar, Jetson Leder-Luis, Leman Akoglu
  https://arxiv.org/abs/2211.02927, Nov. 2024
  

Conference/Journal

• Modeling Collective Behaviors for Human Mobility Anomaly Detection   [  code & data ]
  Haomin Wen, Shurui Cao, Leman Akoglu
  ACM SIGSPATIAL 2025, Minneapolis, MN, USA (Full paper)   (To appear)
  

• Uncertainty-aware Spatio-Temporal Human Mobility Modeling and Anomaly Detection   [  code & data ]
  Haomin Wen, Shurui Cao, Zeeshan Rasheed, Khurram Hassan Shafique, Leman Akoglu
  ACM SIGSPATIAL 2025, Minneapolis, MN, USA (Short paper)   (To appear)
  

• Self-Tuning Self-Supervised Image Anomaly Detection   [  code & data ]
  Jaemin Yoo, Lingxiao Zhao, Leman Akoglu
  ACM SIGKDD 2025, Toronto, Canada  
  

• Trajectory Anomaly Detection with By-Design Complementary Detectors   [  code ]
  Shurui Cao, Leman Akoglu
  SIAM SDM 2025, Alexandria, VA, USA
  

• TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection   [  code & data ]
  Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefania Serral, Jaemin Yoo, Leman Akoglu
  SIAM SDM 2025, Alexandria, VA, USA
  

• On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
  Steven Jecmen, Nihar B. Shah, Fei Fang, Leman Akoglu
  Transactions on Machine Learning Research (TMLR), Jan. 2025  [arXiv]
  

• PARD: Permutation-Invariant Autoregressive Diffusion for Graph Generation  [arXiv]  [  code ]
  Lingxiao Zhao, Xueying Ding, Leman Akoglu
  NeurIPS 2024, Vancouver, Canada
  

• Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors  
  Xueying Ding, Rui Xi, Leman Akoglu
  AAAI/ACM AIES 2024, San Jose, California
  

• Fast Unsupervised Deep Outlier Model Selection with Hypernetworks   [  code & data ]
  Xueying Ding, Yue Zhao, Leman Akoglu
  ACM SIGKDD 2024, Barcelona, Spain  
  

• HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection   [  code & data ]
  Yue Zhao, Leman Akoglu
  AutoML 2024, Paris, France  
  

• Descriptive Kernel Convolution Network with Improved Random Walk Kernel  
  Jeremy Lee, Lingxiao Zhao, Leman Akoglu
  ACM TheWebConf 2024, Singapore  
  

• ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
    [  code]
  Konstantinos Sotiropoulos, Lingxiao Zhao, Pierre J. Liang, Leman Akoglu
  IEEE BigData 2023, Sorrento, Italy (Regular paper)  
  

• Self-Supervision for Unsupervised Anomaly Detection: Pitfalls and Opportunities [Vision Paper]  [arXiv]
  Leman Akoglu and Jaemin Yoo
  IEEE BigData 2023, Sorrento, Italy (Short paper)  
  

• From Detection to Action: a Human-in-the-loop Toolkit for Anomaly Reasoning and Management  [  code ]
  Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
  ACM ICAIF 2023 (International Conference on AI in Finance), NYC, US
  

• Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
  Jaemin Yoo, Tiancheng Zhao, Leman Akoglu
  Transactions on Machine Learning Research (TMLR), July 2023
  

• DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection   [  code]
  Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu
  ECML PKDD 2023, Turin, Italy
  

• Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series  [arXiv]
  Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi, Jonathan Elmer, Christos Faloutsos, Leman Akoglu
  Journal of Biomedical Informatics, Jan. 2023  
  

• Density of States for Fast Embedding Node-Attributed Graphs  
  Lingxiao Zhao, Saurabh S. Sawlani, Leman Akoglu
  Knowledge and Information Systems (KAIS), Jan. 2023  
  

• The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies  [arXiv]  [  code & data ]
  Martin Q. Ma, Yue Zhao, Xiaorong Zhang, Leman Akoglu
  ACM SigKDD Explorations Newsletter, June 2023  
  

• Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution  [arXiv]  [  code & data ]
  Xueying Ding, Lingxiao Zhao, Leman Akoglu
  NeurIPS 2022, New Orleans, LA, USA  
  

• A Practical, Progressively-Expressive GNN  [arXiv]  [  code ]
  Lingxiao Zhao, Louis Härtel, Neil Shah, Leman Akoglu
  NeurIPS 2022, New Orleans, LA, USA  
  

• Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection  
  Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z. Sheng,
  Leman Akoglu, Charu C. Aggarwal
  NeurIPS 2022, New Orleans, LA, USA  
  

• Toward Unsupervised Outlier Model Selection  [  project page ]
  Yue Zhao, Sean Zhang, Leman Akoglu
  IEEE ICDM 2022, Orlando, FL, USA  (Full paper)  
  

• D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments  [  code ]  [  data ]  [arXiv]
  Shuli Jiang, Robson L. F. Cordeiro, Leman Akoglu
  IEEE ICDM 2022, Orlando, FL, USA  (Short paper)  
  

• Graph-level Anomaly Detection with Unsupervised GNNs  [  code & data ]  [arXiv]
  Lingxiao Zhao, Saurabh Sawlani, Arvind Srinivasan, Leman Akoglu
  IEEE ICDM 2022, Orlando, FL, USA  (Short paper)  
  

• Summarizing Labeled Multi-Graphs  [  code]
  Dimitris Berberidis, Pierre J. Liang, Leman Akoglu
  ECML PKDD 2022, Grenoble, France  
  

• Sparx: Distributed Outlier Detection at Scale  [  code]  [arXiv]
  Sean Zhang, Varun Ursekar, Leman Akoglu
  ACM SIGKDD 2022 (Applied Data Science Track), Washington, DC  
  

• Detecting Anomalous Graphs in Labeled Multi-Graph Databases
  Hung Nguyen, Pierre J. Liang, Leman Akoglu
  ACM Transactions on Knowledge Discovery from Data (TKDD), April 2022.
  

• From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
  Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah
  ICLR 2022, Virtual  
  

• MetaOD: Automating Outlier Detection via Meta-Learning  [  project page ]
  Yue Zhao, Ryan Rossi, Leman Akoglu
  NeurIPS 2021, Virtual  
  

• A Comprehensive Survey on Graph Anomaly Detection with Deep Learning  [arXiv]
  Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu
  IEEE Transactions on Knowledge and Data Engineering, Sep. 2021
  

• Fast Attributed Graph Embedding via Density of States  [  project page ]  [arXiv]
  Saurabh Sawlani, Lingxiao Zhao, Leman Akoglu
  IEEE ICDM 2021, Virtual  (Full paper)  
  Invited to KAIS Journal Special Issue (ICDM Best papers)
  

• Anomaly Mining - Past, Present and Future
  Leman Akoglu
  IJCAI 2021 Early Career Spotlight Talk Position Paper, Aug. 2021
  

• On Using Classification Datasets to Evaluate Graph Outlier Detection:
  Peculiar Observations and New Insights  [arXiv]
  Lingxiao Zhao  &  Leman Akoglu
  Big Data Journal, Special Issue on Evaluation and Experimental Design in Data Min. and Mach. Learn., Dec. 2021
  

• GAWD: Graph Anomaly Detection in Weighted Directed Graph Databases  
  Meng-Chieh Lee, Hung T. Nguyen, Dimitris Berberidis, Vincent S. Tseng, Leman Akoglu
  IEEE/ACM ASONAM 2021, Virtual  
  

• FairOD: Fairness-aware Outlier Detection  [arXiv]  [slides]
  Shubhranshu Shekhar, Neil Shah, Leman Akoglu
  AAAI/ACM AIES 2021, Virtual  (Oral presentation)
  

• SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection  [arXiv]
  Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, Leman Akoglu
  MLSys 2021, Virtual
  

• AutoAudit: Mining Accounting and Time-Evolving Graphs (short paper)  [arXiv]
  Meng-Chieh Lee, Yue Zhao, Aluna Wang, Pierre J. Liang, Leman Akoglu, Vincent S. Tseng, Christos Faloutsos
  IEEE BigData 2020, Virtual
  

• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Design Choices  [arXiv]
  Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
  NeurIPS 2020, Virtual
  

• End-to-End Continual Rare-Class Recognition with Emerging Novel Subclasses   [  data & code]
  Hung Nguyen, Xuejian Wang, Leman Akoglu
  ACM Transactions on Knowledge Discovery from Data (TKDD), May 2020.
  

• PairNorm: Tackling Oversmoothing in GNNs  [arXiv] [   code]
  Lingxiao Zhao  &  Leman Akoglu
  ICLR 2020, Addis Ababa, Ethiopia  
  

• Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection  [arXiv]
  Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo
  NeurIPS 2019, Vancouver, Canada  
  

• Continual Rare-Class Recognition with Emerging Novel Subclasses  [arXiv]   [  data & code]
  Hung Nguyen, Xuejian Wang, Leman Akoglu
  ECML PKDD 2019, Würzburg, Germany  
  

• Bot Detection: Will Focusing on Recall Cause Overall Performance Deterioration?
  Tahora H. Nazer, Matthew Davis, Mansooreh Karami, Leman Akoglu, David Koelle, Huan Liu
  SBP-BRiMS 2019, Washington DC, USA  
  

• BuSCOPE: Fusing Individual & Aggregated Mobility Behavior for “Live” Smart City Services
  Lakmal Meegahapola, Thivya Kandappu, Kasthuri Jayarajah, Leman Akoglu, Shili Xiang, Archan Misra
  ACM MobiSys 2019, Seoul, Korea  
  

• ContraVis: Contrastive and Visual Topic Modeling for Comparing Document Collections  [  data & code]
  Tuan Le  &  Leman Akoglu
  TheWebConf 2019, San Francisco, CA   (Full paper)
  

• Learning On-The-Job to Re-rank Anomalies from Top-1 Feedback  [  project page ]
  Hemank Lamba  &  Leman Akoglu
  SIAM SDM 2019, Calgary, Canada  
  Best Research Paper Award    

• A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification  
  [arXiv]  [  project page ]
  Xuan Wu^∗, Lingxiao Zhao^∗, Leman Akoglu
  ACM CIKM 2018, Turin, Italy  
  

• Online Localized Change Detection for Sensor Data on a Graph  
  Bryan Hooi, Leman Akoglu, Dhivya Eswaran, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, Christos Faloutsos
  ACM CIKM 2018, Turin, Italy  
  

• Incorporating Privileged Information to Unsupervised Anomaly Detection   [arXiv]  [  project page ]
  Shubhranshu Shekhar  &  Leman Akoglu
  ECML PKDD 2018, Dublin, Ireland  
  Best Student Machine Learning Paper Runner-up Award

• Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud  [  data & code]  [ project_page ]
  Meghanath Macha, Deepak Pai, Leman Akoglu
  ECML PKDD 2018, Dublin, Ireland  
  

• Beyond Outlier Detection: LookOut for Pictorial Explanation  [  data & code]
  Nikhil Gupta, Dhivya Eswaran, Neil Shah, Leman Akoglu, Christos Faloutsos
  ECML PKDD 2018, Dublin, Ireland  
  Also appearing at the 5th ACM KDD 2018 Workshop on Outlier Detection De-constructed

• Explaining Anomalies in Groups with Characterizing Subspace Rules   [arXiv]  [  data  &  code]
  Meghanath Macha  &  Leman Akoglu
  ECML PKDD 2018 (Journal Track), Dublin, Ireland  
  Data Mining and Knowledge Discovery (10.1007/s10618-018-0585-7), pp 1-37, Aug 2018.

• A Lens into Employee Peer Reviews via Sentiment-Aspect Modeling  [ code]
  Abhinav Maurya, Leman Akoglu, Ramayya Krishnan, Daniel Bay
  ACM/IEEE ASONAM 2018 (Industry Track), Barcelona, Spain  
  

• xStream: Outlier Dete‘x’ion in Feature-Evolving Data Streams  [  data  &  code]
  Emaad A. Manzoor, Hemank Lamba, Leman Akoglu
  ACM SIGKDD 2018, London, UK  
  

• Robust Semi-Supervised Learning on Multiple Networks with Noise  [  data  &  code]
  Junting Ye  &  Leman Akoglu
  PAKDD 2018, Melbourne, Australia  
  
  

• Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization
  Bryan Perozzi  &  Leman Akoglu
  ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 2, Article 24, Jan 2018.
  

• RUSH! Targeted Time-limited Coupons via Purchase Forecasts  [  code]   [poster]
  Emaad A. Manzoor  &  Leman Akoglu
  ACM SIGKDD 2017, Halifax, Canada  
  

• Ranking in Heterogeneous Networks with Geo-Location Information  [  code]   [slides]
  Abhinav Mishra  &  Leman Akoglu
  SIAM SDM 2017, Houston, TX, USA  
  
  

• Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset  
  Sokratis Vidros, Constantinos Kolias, Georgios Kambourakis, Leman Akoglu
  Future Internet, 9(1), 6; doi:10.3390/fi9010006, Mar 2017.  
  

• A Domain-Agnostic Approach to Spam-URL Detection via Redirects  [ data]   [slides]
  Heeyoung Kwon, Mirza Basim Baig, Leman Akoglu
  PAKDD 2017, Jeju, South Korea  
  

• Ties That Bind - Characterizing Classes by Attributes and Social Ties  [   project page]
  Aria Rezaei, Bryan Perozzi, Leman Akoglu
  WWW 2017 Companion, Alternate Web Science Track, Perth, AU  
  

• Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective  
  [arXiv]   [   project page]   [  ODDS data repo]  
  Shebuti Rayana, Wen Zhong, Leman Akoglu
  IEEE ICDM 2016, Barcelona, Spain

• Optimizing Network Robustness by Edge Rewiring: A General Framework  
  Hau Chan  &  Leman Akoglu
  Data Mining and Knowledge Discovery (DAMI), Volume 30, Issue 5, pp 1395–1425, Sep 2016.  
  

• Fast Memory-Efficient Anomaly Detection in Streaming Heterogeneous Graphs (full)  [   project page]
  Emaad A. Manzoor, Sadegh M. Milajerdi, Leman Akoglu
  ACM SIGKDD 2016, San Francisco, CA  

• Temporal Opinion Spam Detection by Multivariate Indicative Signals [arXiv]   [  code]   [poster]
  Junting Ye, Santhosh Kumar, Leman Akoglu
  ICWSM 2016, Cologne, Germany  
  

  

• GOTCHA! Network-based Fraud Detection for Social Security Fraud  
  Veronique Van Vlasselaer, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens
  Management Science, 63 (9), MS-14-00232.R4, 2016.  
  

• Less is More: Building Selective Anomaly Ensembles  
  Shebuti Rayana  &  Leman Akoglu
  ACM Transactions on Knowledge Discovery from Data (TKDD), 10(4): 42, 2016.  
  

• Scalable Anomaly Ranking of Attributed Neighborhoods   [   project page]  
  Bryan Perozzi  &  Leman Akoglu
  SIAM SDM 2016, Miami, Florida  
  Best Research Paper Runner-up Award

• Collective Opinion Spam Detection using Active Inference  
  Shebuti Rayana  &  Leman Akoglu
  SIAM SDM 2016, Miami, Florida  
  

• BIRDNEST: Bayesian Inference for Ratings-Fraud Detection  
  Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
  SIAM SDM 2016, Miami, Florida 
  

• Optimizing Network Robustness by Edge Rewiring: A General Framework  
  Hau Chan  &  Leman Akoglu
  ECML PKDD 2016, Journal Track  
  

  

• Graph-based Anomaly Detection and Description: A Survey   [bibtex]
  Leman Akoglu, Hanghang Tong, Danai Koutra
  Data Mining and Knowledge Discovery (DAMI), 29 (3), May 2015.

• Discovering Opinion Spammer Groups by Network Footprints   [   code]   [slides]
  Junting Ye  &  Leman Akoglu
  ECML PKDD 2015, Porto, Portugal
  

• Collective Opinion Spam Detection: Bridging Review Networks and Metadata   [   project page]   [slides]
  Shebuti Rayana  &  Leman Akoglu
  ACM SIGKDD 2015, Sydney, AU
  

• AFRAID: Fraud Detection via Active Inference in Time-evolving Social Networks  
  Veronique Van Vlasselaer, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens.
  IEEE/ACM ASONAM 2015, Paris, France
  

• Effects of Tag Usage on Question Response Time: Analysis and Prediction in StackOverflow  
  Vasudev Bhat, Adheesh Gokhale, Ravi Jadhav, Jagat Sastry Pudipeddi, Leman Akoglu
  Social Network Analysis and Mining, (SNAM-D-14-00101R1) Springer, 2015.
  

• Correlation of Node Importance Measures: An Empirical Study through Graph Robustness    
  Mirza Basim Baig  &  Leman Akoglu
  WWW 2015 Companion, Web Science Track (13.8%), Florence, Italy
  Invited to a special issue of the Journal of Web Science
  
  

• APATE: A Novel Approach for Automated Credit Card Transaction Fraud Detection using Network-Based Extensions.  
  Veronique Van Vlasselaer, Cristian Bravo, Olivier Caelen, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens.
  Decision Support Systems, May 2015.

• Less is More: Building Selective Anomaly Ensembles   [   project page]
  (with Application to Event Detection in Temporal Graphs)  
  Shebuti Rayana  &  Leman Akoglu
  SIAM SDM 2015, Vancouver, BC, Canada (Oral presentation)

• Where Graph Topology Matters: The Robust Subgraph Problem  
  Hau Chan, Shuchu Han, Leman Akoglu
  SIAM SDM 2015, Vancouver, BC, Canada (Oral presentation)
  Best Research Paper Award     Invited to Statistical Analysis and Data Mining

• Guilt-by-Constellation: Fraud Detection by Suspicious Clique Memberships.  
  Veronique Van Vlasselaer, Leman Akoglu, Tina Eliassi-Rad, Monique Snoeck, Bart Baesens.
  HICSS 2015, Kauai, HI

• Fast Nearest Neighbor Search on Large Time-Evolving Graphs.   [slides]
  Leman Akoglu, Rohit Khandekar, Vibhore Kumar, Srinivasan Parthasarathy, Deepak Rajan, Kun-Lung Wu.
  ECML PKDD 2014, Nancy, France

• Focused Clustering and Outlier Detection in Large Attributed Graphs.   [slides]   [  code]   [project page]
  Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sanchez, Emmanuel Müller.
  ACM SIGKDD 2014, NYC, New York

• Min(e)d Your Tags: Analysis of Question Response Time in StackOverflow. (Full paper)   [slides]
  Vasudev Bhat, Adheesh Gokhale, Ravi Jadhav, Jagat S. Pudipeddi, Leman Akoglu.
  IEEE/ACM ASONAM 2014, Beijing, China
  Invited to appear in SNAM (Social Network Analysis and Mining) journal by Springer (ASONAM best papers)

• Joint Voting Prediction for Questions and Answers in CQA.   [MIT Tech Review]
  Yuan Yao, Hanghang Tong, Tao Xie, Leman Akoglu, Feng Xu, Jian Lu.
  IEEE/ACM ASONAM 2014, Beijing, China
  Invited to appear in SNAM (Social Network Analysis and Mining) journal by Springer (ASONAM best papers)

• Fast Information-Theoretic Agglomerative Co-Clustering     [supplementary]   [code]
  Tiantian Gao  &  Leman Akoglu.
  ADC 2014, Brisbane, Australia
  Best Paper Award

• Quantifying Political Polarity based on Bipartite Opinion Networks   [code]
  Leman Akoglu
  ICWSM 2014, Ann Arbor, Michigan

• ConnotationWordNet: Learning Connotation of the Word+Sense Network (Full paper)  
  Jun Seok Kang, Song Feng, Leman Akoglu, Yejin Choi.
  ACL 2014, Baltimore, Maryland

• Make It or Break It: Manipulating Robustness in Large Networks   [slides]   [tech report]
  Hau Chan, Leman Akoglu, Hanghang Tong
  SIAM SDM 2014, Philadelphia, PA

• User Churn in Focused Q&A Sites: Characterizations and Prediction    
  Jagat S. Pudipeddi, Leman Akoglu, Hanghang Tong
  WWW 2014, Seoul, Korea

• External Evaluation of Topic Models: A Graph Mining Approach     [long version]  
  Hau Chan  &  Leman Akoglu
  IEEE ICDM 2013, Dallas, Texas

• Sex Differences in the Human Connectome    
  Vivek Kulkarni, Jagat S. Pudipeddi, Leman Akoglu, Joshua Vogelstein, Jacob Vogelstein, Sephira Ryman, Rex E. Jung
  BHI 2013, Maebashi, Japan

• Opinion Fraud Detection in Online Reviews using Network Effects      
  Leman Akoglu, Rishi Chandy, Christos Faloutsos
  ICWSM 2013, Boston, MA

• Do more views of a graph help? Community Detection and Clustering in Multi-Graphs    
  Evangelos E. Papalexakis, Leman Akoglu, Dino Ienco
  FUSION 2013, Istanbul, Turkey

• Mining Connection Pathways for Marked Nodes in Large Graphs      
  Leman Akoglu, Jilles Vreeken, Hanghang Tong, Duen Horng Chau, Nikolaj Tatti, Christos Faloutsos
  SIAM SDM 2013, Austin, Texas

• Fast and Reliable Anomaly Detection in Categoric Data    
  Leman Akoglu, Hanghang Tong, Jilles Vreeken, Christos Faloutsos
  ACM CIKM 2012, Maui, Hawaii

• TourViz: Interactive Visualization of Connection Pathways in Large Graphs   (Demo paper)   
  Duen Horng Chau, Leman Akoglu, Jilles Vreeken, Hanghang Tong, Christos Faloutsos
  ACM SIGKDD 2012, Beijing, China

• RolX: Structural Role Extraction & Mining in Large Graphs   
  Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Lei Li, Christos Faloutsos.
  ACM SIGKDD 2012, Beijing, China

• Quantifying Reciprocity in Large Weighted Communication Networks       
  Leman Akoglu, Pedro Olmo Vaz de Melo, Christos Faloutsos.
  PAKDD 2012, Kuala Lumpur, Malaysia
  Received the PAKDD Student Travel Grant Award

• OPAvion: Mining and visualization in large graphs   (Demo paper)   
  Leman Akoglu*, Duen Horng Chau*, U Kang*, Danai Koutra*, Christos Faloutsos
  SIGMOD 2012, Scottsdale, Arizona

• PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs      
  Leman Akoglu, Hanghang Tong, Brendan Meeder, Christos Faloutsos.
  SIAM SDM 2012, Anaheim, CA

• It's Who You Know: Graph Mining Using Recursive Structural Features   
  Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, Christos Faloutsos.
  ACM SIGKDD 2011, San Diego, CA

• Patterns on the Connected Components of Terabyte-Scale Graphs   
  U Kang, Mary McGlohon, Leman Akoglu, Christos Faloutsos.
  IEEE ICDM 2010, Sydney, Australia

• Surprising Patterns for the Call Duration Distribution of Mobile Phone Users   
  Pedro Olmo Vaz de Melo, Leman Akoglu, Christos Faloutsos, Antonio Loureiro.
  ECML PKDD 2010, Barcelona, Spain
  Received the Google ECML/PKDD 2010 Conference and Travel Grant Award for Women in CS

• MetricForensics: A Multi-Level Approach for Mining Volatile Graphs   
  Keith Henderson, Tina Eliassi-Rad, Christos Faloutsos, Leman Akoglu, Lei Li, Koji Maruhashi, B. Aditya Prakash, Hanghang Tong.
  ACM SIGKDD 2010, Washington, DC

• OddBall: Spotting Anomalies in Weighted Graphs       
  Leman Akoglu, Mary McGlohon, Christos Faloutsos.
  PAKDD 2010, Hyderabad, India
  Best Research Paper Award
  PAKDD 2020 The Most Influential Paper Award

• RTG: A Recursive Realistic Graph Generator using Random Typing       
  Leman Akoglu and Christos Faloutsos.
  ECML PKDD 2009, Bled, Slovenia
  Best Knowledge Discovery Paper Award

• Large Human Communication Networks: Patterns and a Utility-Driven Generator   
  Nan Du, Christos Faloutsos, Bai Wang, Leman Akoglu.
  ACM SIGKDD 2009, Paris, France

• RTM : Laws and a Recursive Generator for Weighted Time-Evolving Graphs     
  Leman Akoglu, Mary McGlohon, Christos Faloutsos.
  ICDM 2008, Pisa, Italy

• Weighted Graphs and Disconnected Components: Patterns and a Generator     
  Mary McGlohon, Leman Akoglu, Christos Faloutsos.
  ACM SIGKDD 2008, Las Vegas, NV

Workshop

• EdgeCentric: Anomaly Detection in Edge-Attributed Networks   [  code]
  Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos.
  IEEE ICDM Workshop on Data Mining for Cyber Security, Barcelona, Spain, Dec. 2016.  

• The Politics of Routing: Investigating the Relationship Between AS Connectivity and Internet Freedom  
  Rachee Singh, Hyungjoon Koo, Najmehalsadat Miramirkhani, Fahimeh Mirhaj, Leman Akoglu, Phillipa Gill.
  FOCI/USENIX, Austin, TX, USA, Aug. 2016.  

• An Ensemble Approach for Event Detection and Characterization in Dynamic Graphs      
  Shebuti Rayana, Leman Akoglu
  ACM SIGKDD 2nd Workshop on Outlier Detection and Description (ODD^2@KDD 2014), NYC, NY, Aug. 2014

• Proceedings of the KDD 2014 Workshop on Outlier Detection and Description under Data Diversity      
  Charu Aggarwal, Leman Akoglu, Emmanuel Muller, Raymond T. Ng (editors)
  ACM SIGKDD 2nd Workshop on Outlier Detection and Description (ODD^2@KDD 2014), NYC, NY, Aug. 2014

• Proceedings of the KDD 2013 Workshop on Outlier Detection and Description      
  Leman Akoglu, Emmanuel Muller, Jilles Vreeken (editors)
  ACM SIGKDD 1st Workshop on Outlier Detection and Description (ODD@KDD 2013), Chicago, IL, USA, Aug. 2013.

• Mining and Visualizing Connection Pathways in Large Information Networks   (poster)   
  Leman Akoglu, Jilles Vreeken, Hanghang Tong, Duen Horng Chau, Christos Faloutsos
  WIN: Workshop on Information in Networks, New York, USA, Sep. 2012.

• ValuePick: Towards a Value-Oriented Dual-Goal Recommender System     
  Leman Akoglu and Christos Faloutsos.
  ICDM Workshop on Optimization Based Methods for Emerging Data Mining Problems, Sydney, Australia, Dec. 2010.

• Event Detection in Time Series of Mobile Communication Graphs     
  Leman Akoglu and Christos Faloutsos.
  27th Army Science Conference, Orlando, Florida, Dec. 2010.

• Structure, Tie Persistence and Event Detection in Large Phone and SMS Networks     
  Leman Akoglu and Bhavana Dalvi.
  ACM SIGKDD Workshop on Mining and Learning with Graphs, Washington, DC, July 2010.

Book Chapter

• Statistical Properties of Social Networks. Chapter 2 in "Social Network Data Analytics"
  Mary McGlohon, Leman Akoglu, Christos Faloutsos. (Edited by Charu C. Aggarwal)

Technical Reports

• Islands and Bridges: Making Sense of Marked Nodes in Large Graphs     
  Leman Akoglu, Jilles Vreeken, Hanghang Tong, Duen Horng Chau, Nikolaj Tatti, Christos Faloutsos. Carnegie Mellon University Technical Report: CMU-CS-12-124.

• Anomaly Detection in Large Graphs     
  Leman Akoglu, Mary McGlohon, Christos Faloutsos. Carnegie Mellon University Technical Report: CMU-CS-09-173.

• RTM : Laws and a Recursive Generator for Weighted Time-Evolving Graphs   
  Leman Akoglu, Mary McGlohon, Christos Faloutsos. Carnegie Mellon University Technical Report: CMU-CS-08-161.

Code

• StreamSpot: finding anomalies among a collection of streaming heterogeneous graphs

• CARE: a sequential ensemble algorithm for outlier detection

• HINside: ranking nodes in a heterogeneous network with node-location information

• EdgeCentric: finding distributional anomalies for spotting suspicious user behavior.

• TemporalOpinionSpam: event detection in multivariate indicative signals of opinion spam.

• SpammerGroups: computing NFS (network footprint score) and identifying groups of spammers that target the same services within the same campaign.

• GRASP-RLS: finding robust subgraphs of a given size (in Matlab).

• FraudEagle: finding fake reviewers in bipartite review networks (in Matlab).

• CoClusLSH: fast bottom-up (agglomerative) hierarchical co-clustering (in Matlab).

• RTG: generate realistic, time-evolving, weighted graphs (in Matlab).

• OddBall: spot anomalous nodes in weighted, unlabeled graphs (in Python and Matlab).
     >>> Here is a Python-free 'light' version of Oddball.

• PICS: partition attributed graphs into cohesive clusters (in Matlab).

• Dot2Dot: summarize (by a forest of connection trees) a set of nodes in a graph (in Matlab).

• CompreX: find anomalies in categorical/numerical databases (in Matlab).

Ph.D. Thesis

• Mining and Modeling Real-world Networks: Patterns, Anomalies, and Tools.
  Leman Akoglu, Doctoral Dissertation, Carnegie Mellon University, 2012

Patents

• Vertex-proximity query processing in large time-evolving graphs
  Inventors: Rohit M Khandekar, Kun-Lung Wu, Leman Akoglu, Deepak Rajan, Vibhore Kumar, Srinivasan Parthasarathy.
  IBM T. J. Watson Research, filed December 2011.
  

• Anomaly detection in complex datasets using compression
  Inventors: Hanghang Tong, Leman Akoglu, Ching-Yung Lin, Ravi Konuru.
  IBM T. J. Watson Research, two patents filed December 2011.