Publications

• Revisiting Link Prediction: A Data Perspective
  Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang
  International Conference on Learning Representations (ICLR) 2024.


• A Topological Perspective on Demystifying GNN-based Link Prediction Performance
  Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, Tyler Derr
  International Conference on Learning Representations (ICLR) 2024.


• Learning from Graphs without Explicit Graph Machine Learning Models
  Tong Zhao, Neil Shah, Elham Ghazizadeh
  International Conference on Learning Representations (ICLR) Tiny Papers 2024.


• Large-Scale Graph Neural Networks: The Past and New Frontiers
  Rui Xue, Haoyu Han, Tong Zhao, Neil Shah, Jiliang Tang, Xiaorui Liu
  AAAI Conference on Artificial Intelligence (AAAI) Tutorials 2024.


• Large-Scale Graph Neural Networks: The Past and New Frontiers
  Rui Xue, Haoyu Han, Tong Zhao, Neil Shah, Jiliang Tang, Xiaorui Liu
  SIAM International Conference on Data Mining (SDM) Tutorials 2024.


• Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
  Juanhui Li, Harry Shomer, Haitao Mao, Shenglai Zeng, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin
  Conference on Neural Information Processing Systems (NeurIPS) 2023.


• Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
  Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang
  Conference on Neural Information Processing Systems (NeurIPS) 2023.


• GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
  Mingxuan Ju, Tong Zhao, Wenhao Yu, Neil Shah, Yanfang Ye
  Conference on Neural Information Processing Systems (NeurIPS) 2023.


• Graph Data Augmentation for Graph Machine Learning: A Survey
  Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Gunnemann, Neil Shah, Meng Jiang
  IEEE Data Engineering Bulletin 2023.

CARL-G: Clustering-Accelerated Representation Learning on Graphs
William Shiao, Uday Singh Saini, Yozen Liu, Tong Zhao, Neil Shah, Evangelos Papalexakis
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023.


• Sketch-Based Anomaly Detection in Streaming Graphs
  Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip Yu, Bryan Hooi
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023.


• Large-Scale Graph Neural Networks: The Past and New Frontiers
  Rui Xue, Haoyu Han, Tong Zhao, Neil Shah, Jiliang Tang, Xiaorui Liu
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Tutorials 2023.


• Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
  Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin
  Annual Meeting of the Association of Computational Linguistics (ACL) 2023.


• Linkless Link prediction via Relational Distillation
  Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh Chawla, Neil Shah, Tong Zhao
  International Conference on Machine Learning (ICML) 2023.


• Embedding-based Retrieval in Friend Recommendation
  Jiahui Shi, Vivek Chaurasiya, Yozen Liu, Shubham Vij, Yan Wu, Satya Kanduri, Neil Shah, Peicheng Yu, Nik Srivastava, Lei Shi, Ganesh Venkataraman, Jun Yu
  SIGIR Symposium on IR in Practice (SIRIP) 2023.


• MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
  Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, Neil Shah
  International Conference on Learning Representations (ICLR) 2023.


• Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
  Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, Chuxu Zhang
  International Conference on Learning Representations (ICLR) 2023.


• Link Prediction with Non-Contrastive Learning
  William Shiao, Zhichun Guo, Tong Zhao, Vagelis Papalexakis, Yozen Liu, Neil Shah
  International Conference on Learning Representations (ICLR) 2023.


• Empowering Graph Representation Learning with Test-Time Graph Transformation
  Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah
  International Conference on Learning Representations (ICLR) 2023.


• Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference
  Yiwei Wang, Bryan Hooi, Yozen Liu, Neil Shah
  ACM International Conference on Web Search and Data Mining (WSDM) 2023.


• Augmentation Methods for Graph Learning
  Tong Zhao, Kaize Ding, Wei Jin, Gang Liu, Meng Jiang, Neil Shah
  SIAM International Conference on Data Mining (SDM) Tutorials 2023.


• Flashlight: Scalable Link Prediction with Effective Decoders
  Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah
  Learning on Graphs Conference (LoG) 2023.


• Explaining Graph Neural Networks with Structure-Aware Cooperative Games
  Shichang Zhang, Yozen Liu, Neil Shah, Yizhou Sun
  Conference on Neural Information Processing Systems (NeurIPS) 2022.
  Code


• A Practical, Progressively Expressive Graph Neural Network
  Lingxiao Zhao, Louis Haertel, Neil Shah, Leman Akoglu
  Conference on Neural Information Processing Systems (NeurIPS) 2022.


• Imbalanced Graph Classification via Graph-of-Graph Neural Networks
  Yu Wang, Yuying Zhao, Neil Shah, Tyler Derr
  ACM International Conference on Information and Knowledge Management (CIKM) 2022.
  Code


• Predicting Future Location Categories of Users in a Large Social Platform
  Raiyan Baten, Yozen Liu, Heinrich Peters, Francesco Barbieri, Neil Shah, Leonardo Neves, Maarten Bos
  AAAI International Conference on Web and Social Media (ICWSM) 2023.


• Sunshine with a Chance of Smiles: How does Weather Impact Sentiment on Social Media?
  Julie Jiang, Nils Murrugarra-Llerena, Maarten Bos, Yozen Liu, Neil Shah, Leonardo Neves, Francesco Barbieri
  AAAI International Conference on Web and Social Media (ICWSM) 2022.


• Graph-less Neural Networks: Teaching Old MLPs new Tricks via Distillation
  Shichang Zhang, Yozen Liu, Yizhou Sun, Neil Shah
  International Conference on Learning Representations (ICLR) 2022.
  Code


• Graph Condensation for Graph Neural Networks
  Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah
  International Conference on Learning Representations (ICLR) 2022.
  Code


• From Stars to Subgraphs: Uplifting any GNN with Local Structure Awareness
  Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah
  International Conference on Learning Representations (ICLR) 2022.
  Code


• Is Homophily a Necessity for Graph Neural Networks?
  Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang
  International Conference on Learning Representations (ICLR) 2022.


• Automated Self-Supervised Learning for Graphs
  Wei Jin, Xiaorui Liu, Xiaoyu Zhao, Yao Ma, Neil Shah, Jiliang Tang
  International Conference on Learning Representations (ICLR) 2022.
  Code


• Ranking Friend Stories on Social Platforms with Edge-Contextual Local Graph Convolutions
  Xianfeng Tang, Yozen Liu, Xinran He, Suhang Wang, Neil Shah
  ACM International Conference on Web Search and Data Mining (WSDM) 2022.


• Attributed Graph Modeling with Vertex Replacement Grammars
  Satyaki Sikdar, Neil Shah, Tim Weninger
  ACM International Conference on Web Search and Data Mining (WSDM) 2022.
  Code


• Finding a Concise, Precise and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
  Hyeonjeong Shin, Taehyung Kwon, Neil Shah, Kijung Shin
  ACM International Conference on Web Search and Data Mining (WSDM) 2022.
  Code


• Action Sequence Augmentation for Early Graph-based Anomaly Detection
  Tong Zhao, Bo Ni, Wenhao Yu, Zhichun Guo, Neil Shah, Meng Jiang
  ACM International Conference on Information and Knowledge Management (CIKM) 2021.


• A Unified View on Graph Neural Networks as Graph Signal Denoising
  Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah
  ACM International Conference on Information and Knowledge Management (CIKM) 2021.


• Niche Detection in User Content Consumption Data
  Ekta Gujral, Leonardo Neves, Evangelos Papalexakis, Neil Shah
  ACM International Conference on Information and Knowledge Management (CIKM) 2021.


• A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning
  Tong Zhao, Tianwen Jiang, Neil Shah, Meng Jiang
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2021.
  Code


• FairOD: Fairness-aware Outlier Detection
  Shubhranshu Shekhar, Neil Shah, Leman Akoglu
  AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2021.
  Code Video


• Online Communication Shifts in the Midst of the Covid-19 Pandemic: A Case Study on Snapchat
  Qi Yang, Weinan Wang, Lucas Pierce, Rajan Vaish, Xiaolin Shi, Neil Shah
  AAAI International Conference on Web and Social Media (ICWSM) 2021.
  Video


• CEAM: The Effectiveness of Cyclic and Ephemeral Attention Models of User Behavior on Social Platforms
  Farhan Asif Chowdhury, Yozen Liu, Koustuv Saha, Nicholas Vincent, Leonardo Neves, Neil Shah, Maarten Bos
  AAAI International Conference on Web and Social Media (ICWSM) 2021.
  Video


• Graph Neural Networks for Friend Ranking in Large-scale Social Platforms
  Aravind Sankar, Yozen Liu, Jun Yu, Neil Shah
  The Web Conference (WWW) 2021.
  Code Video


• AdverTiming Matters: Examining User Ad Consumption for Effective Ad Allocations on Social Media
  Koustuv Saha, Yozen Liu, Nicholas Vincent, Farhan Asif Chowdhury, Leonardo Neves, Neil Shah, Maarten Bos
  ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) 2021.
  Video


• Data Augmentation for Graph Neural Networks
  Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah
  AAAI Conference on Artificial Intelligence (AAAI) 2021.
  Code Video


• The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks
  Brihi Joshi, Francesco Barbieri, Neil Shah, Leonardo Neves
  International Conference on Computational Linguistics (COLING) 2020.


• Social Factors in Closed-Network Content Consumption
  Parisa Kaghazgaran, Maarten Bos, Leonardo Neves, Neil Shah
  ACM International Conference on Information and Knowledge Management (CIKM) 2020.


• Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks
  Neil Shah
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Workshop on Mining and Learning with Graphs 2020.
  Code


• Identifying Misinformation from Website Screenshots
  Sara Abdali, Rutuja Gurav, Siddharth Menon, Daniel Fonseca, Negin Entezari, Neil Shah, Evangelos Papalexakis
  AAAI International Conference on Web and Social Media (ICWSM) 2021.
  Video


• Semi-Supervised Multi-aspect Misinformation Detection with Hierarchical Joint Decomposition
  Sara Abdali, Neil Shah and Evangelos Papalexakis
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2020.
  Code


• Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
  Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra and Suhang Wang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020.
  Code


• SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs
  Hamed Nilforoshan, Neil Shah
  IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2019.
  Code


• FARE: Schema-Agnostic Anomaly Detection in Social Event Logs
  Neil Shah
  IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2019.


• Modeling Dwell Time Engagement on Visual Multimedia
  Hemank Lamba, Neil Shah
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019.
  Code Video
  


• Impact of Contextual Factors on Public Snapchat Sharing
  Hana Habib, Neil Shah, Rajan Vaish
  ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) 2019.
  Video
  Best Research Paper Honorable Mention Award
  


• Characterizing and Detecting Livestreaming Chatbots
  Shreya Jain, Dipankar Niranjan, Hemank Lamba, Neil Shah, Ponnurangam Kumaraguru
  IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM) 2019.
  


• False Information on Web and Social Media: A Survey
  Srijan Kumar, Neil Shah
  Social Media Analytics: Advances and Applications, CRC Press 2018.


• Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings
  Gisel Bastidas Guacho, Sara Abdali, Neil Shah, Evangelos E. Papalexakis
  IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM) 2018.
  KDD DSJM'18 version Extended arXiv version


• Beyond Outlier Detection: LookOut for Pictorial Explanation
  Nikhil Gupta, Dhivya Eswaran, Neil Shah, Leman Akoglu, Christos Faloutsos
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018.
  Code
  


• Did We Get It Right? Predicting Query Performance in E-commerce Search
  Rohan Kumar, Mohit Kumar, Neil Shah, Christos Faloutsos
  ACM Special Interest Group on Information Retrieval (SIGIR) Workshop on eCommerce 2018.


• Reducing Large Graphs to Small Supergraphs: A Unified Approach
  Yike Liu, Tara Safavi, Neil Shah, Danai Koutra
  Springer Social Network Analysis and Mining (SNAM) 2018.
  Code Demo
  


• Graph-based Fraud Detection in the Face of Camouflage
  Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, Christos Faloutsos
  ACM Transactions on Knowledge Discovery from Data (TKDD) 2017.
  Code


• On Summarizing Large Scale Dynamic Graphs
  Neil Shah, Danai Koutra, Lisa Jin, Tianmin Zou, Brian Gallagher, Christos Faloutsos
  IEEE Data Engineering Bulletin 2017.


• FLOCK: Combating Astroturfing on Livestreaming Platforms
  Neil Shah
  ACM World Wide Web Conference (WWW) 2017.
  Used in production at Twitch.tv
  


• The Many Faces of Link Fraud
  Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos
  IEEE International Conference on Data Mining (ICDM) 2017.
  Extended arXiv version


• M3A: Model, MetaModel, and Anomaly Detection in Web Searches
  Da-Cheng Juan, Neil Shah, Mingyu Tang, Zhiliang Qian, Diana Marculescu, Christos Faloutsos
  IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2017.
  Used in production at Google
  


• Reducing Million-Node Graphs to a Few Structural Patterns: A Unified Approach
  Yike Liu, Tara Safavi, Neil Shah, Danai Koutra
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Workshop on Mining and Learning with Graphs 2016.
  


• FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
  Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016.
  Code Video
  Best Research Paper Award
  


• EdgeCentric: Anomaly Detection in Edge-Attributed Networks
  Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos
  IEEE International Conference on Data Mining (ICDM) Workshop on Data Mining for Cyber Security 2016.
  Extended arXiv version Code
  Used in production at Flipkart
  


• BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
  Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
  SIAM International Conference on Data Mining (SDM) 2016.
  Code
  


• An Empirical Comparison of the Sumarization Power of Graph Clustering Methods
  Yike Liu, Neil Shah, Danai Koutra
  Neural Information Processing Systems (NIPS) Workshop on Networks in the Social and Information Sciences 2015.
  


• TimeCrunch: Interpretable Dynamic Graph Summarization
  Neil Shah, Danai Koutra, Tianmin Zou, Brian Gallagher, Christos Faloutsos
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2015.
  CodeVideo
  


• s-index: Towards Better Metrics for Quantifying Research Impact
  Neil Shah, Yang Song
  arXiv preprint (2015).
  Code


• DeltaCon: A Principled Massive-Graph Similarity Function with Attribution
  Danai Koutra, Neil Shah, Joshua T. Vogelstein, Brian Gallagher, Christos Faloutsos
  Transactions on Knowledge Discovery from Data (TKDD) 2015.
  Matlab Code C++ Code
• 
  Retweeting activity on Twitter: Signs of Deception
  Maria Giatsoglou, Despoina Chatzakou, Neil Shah, Christos Faloutsos, Athena Vakali
  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2015.
  


• ND-SYNC: Detecting Synchronized Fraud Activities
  Maria Giatsoglou, Despoina Chatzakou, Neil Shah, Alex Beutel, Christos Faloutsos, Athena Vakali
  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2015.
  IC2S2 version


• Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
  Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos
  IEEE International Conference on Data Mining (ICDM) 2014.
  Extended arXiv version Code


• Improving I/O Throughput with PRIMACY: Preconditioning ID-Mapper for Compressing Incompressibility
  Neil Shah, Eric R. Schendel, Sriram Lakshminarasimhan, Saurabh V. Pendse, Terry Rogers, Nagiza F. Samatova
  IEEE Cluster 2012.
  


• Analytics-driven Lossless Data Compression for Rapid In-situ Indexing, Storing, and Querying
  Isha Arkatkar, John Jenkins, Sriram Lakshminarasimhan, Neil Shah, Eric R. Schendel, Stephane Ethier, CS Chang, Jackie Chen, Hemanth Kolla, Scott Klasky, Robert Ross, Nagiza F. Samatova
  International Conference on Database and Expert Systems Applications (DEXA) 2012.
  Extended journal version


• ISOBAR Preconditioner for Effective and High-throughput Lossless Data Compression
  Eric R. Schendel, Ye Jin, Neil Shah, Jackie Chen, CS Chang, Seung-Hoe Ku, Stephane Ethier, Scott Klasky, Rob Latham, Rob Ross, Nagiza F. Samatova
  IEEE International Conference on Data Engineering (ICDE) 2012.
  


• Compressing the Incompressible with ISABELA: In-situ Reduction of Spatio-Temporal Data
  Sriram Lakshminarasimhan, Neil Shah, Stephane Ethier, Seung-Hoe Ku, C.S. Chang, Scott Klasky, Rob Latham, Rob Ross, Nagiza F. Samatova
  Euro-Par 2011.
  Extended journal version


• S-preconditioner for Multi-fold Data Reduction with Guaranteed User-controlled Accuracy
  Ye Jin, Sriram Lakshminarasimhan, Neil Shah, Zhenhuan Gong, CS Chang, Jackie Chen, Stephane Ethier, Hemant Kolla, Seung-Hoe Ku, Scott Klasky, Rob Latham, Rob Ross, Karen Schuchardt, Nagiza F. Samatova
  IEEE International Conference on Data Mining (ICDM) 2011.
  

Automatic and Statistically Robust Spatio-Temporal Detection and Tracking of Fusion Plasma Turbulent Fronts
Neil Shah, Yekaterina Shpanskaya, CS Chang, Seung-Hoe Ku, Anatoli V. Melechko, Nagiza F. Samatova
Scientific Discovery through Advanced Computing (SciDAC) 2010.

pR: Automatic Parallelization of Data-parallel Statistical Computing Codes for R in Hybrid Multi-node and Multi-core Environments
Paul Breimyer, Guruprasad Kora, William Hendrix, Neil Shah, Nagiza F. Samatova
IADIS International Conference on Applied Computing 2009.
Associated useR talk abstract