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Contact Information

Name Neil Shah
Email nshah171@gmail.com
Website https://nshah.net

Experience

  • 2017 - present

    Seattle, WA

    Director of Research, Senior Principal Scientist
    Snap Inc.
    • Lead a horizontal team of researchers and engineers on academic, applied research, and engineering initiatives in user modeling and personalization, with applications to growth, content, ads, and safety.
  • 2013 - 2017

    Pittsburgh, PA

    Graduate Researcher
    Carnegie Mellon University
    • Computer Science Department. Worked on algorithms and applications for anomaly detection in large social graphs.
  • 2016 - 2016

    San Francisco, CA

    Visiting Researcher
    Twitch
    • Worked on anti-abuse technologies as a member of the Science team.
  • 2015 - 2015

    Redmond, WA

    Research Intern
    Microsoft Research
    • Improved metrics and methods for measuring research impact for Microsoft Academic Search.
  • 2014 - 2014

    Livermore, CA

    Research Intern
    Lawrence Livermore National Laboratory
    • Developed algorithms to automatically identify patterns and anomalies in time-evolving graphs.
  • 2012 - 2013

    San Jose, CA

    Software Intern
    IBM Silicon Valley Lab
    • Worked in the IBM BigInsights group on indexing and analytics of system log data.
  • 2009 - 2013

    Raleigh, NC

    Undergraduate Researcher
    North Carolina State University
    • Department of Computer Science. Worked on compressing and indexing large scientific datasets.

Summary

  • Director of Research, Senior Principal Scientist at Snap Inc. I lead a horizontal team of researchers and engineers on academic, applied research, and engineering initiatives in user modeling and personalization, with applications to growth, content, ads, and safety.

Education

  • 2013 - 2017

    Pittsburgh, PA

    PhD
    Carnegie Mellon University
    Computer Science
  • 2013 - 2017

    Pittsburgh, PA

    MS
    Carnegie Mellon University
    Computer Science
  • 2010 - 2013

    Raleigh, NC

    BS
    North Carolina State University
    Computer Science (Minor in Mathematics)
    • GPA: 4.0 (class rank #1), Summa Cum Laude with Honors

Awards and Distinctions

  • ACM CIKM Best Paper Award, 2025
  • North Carolina State University Department of Computer Science Rising Star Award, 2023
  • ACM WSDM Outstanding Service Award, 2022
  • ACM SIGCHI Best Research Paper Honorable Mention Award, 2019
  • Symantec Graduate Research Fellowship Finalist, 2017
  • ACM SIGKDD Best Research Paper Award, 2016
  • National Science Foundation Graduate Research Fellowship, 2013
  • North Carolina State University College of Engineering Senior Award for Scholarly Achievement, 2013
  • North Carolina State University Department of Computer Science Senior Faculty Scholar, 2012
  • National Science Foundation Research Experience for Undergraduates Grant, 2011
  • North Carolina State University College of Engineering Dean’s Research Assistantship, 2011
  • North Carolina State University Caldwell Fellowship, 2011
  • Coca-Cola Scholarship, 2010
  • Zinch Scholarship, 2010
  • National Merit Scholarship, 2010
  • CompTIA Information Technology Merit Award, 2010
  • 2nd place, National Siemens Competition in Math, Science and Technology, 2009
  • 1st place, Regional Siemens Competition in Math, Science and Technology, 2009

Refereed Conference Publications

  • X. Ding, X. Huang, C. Ju, L. Collins, Y. Liu, L. Akoglu, N. Shah, T. Zhao. Hierarchical Token Prepending: Enhancing Information Flow in Decoder-based LLM Embeddings. ACL 2026.
  • X. Huang, X. Ding, M. Ju, Y. Liu, N. Shah, T. Zhao. Threshold Differential Attention for Sink-Free, Ultra-Sparse, and Non-Dispersive Language Modeling. ACL 2026.
  • J. Zhu, M. Ju, Y. Liu, S. Vij, D. Koutra, N. Shah, T. Zhao. Beyond Unimodal Perspectives: Generative Retrieval with Multimodal Semantics. SIGIR 2026.
  • C. Ju, T. Zhao, L. Neves, L. Collins, B. Kumar, et al. Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices. SIGIR 2026.
  • G. Lee, B. Kumar, C. Ju, T. Zhao, K. Shin, N. Shah, L. Collins. Sequential Data Augmentation for Generative Recommendation. WSDM 2026.
  • Z. Zheng, Y. Zhu, H. Liu, C. Ju, T. Zhao, N. Shah, J. Li. Pretrained Language Model based Cold-Start Recommendation with Meta-Item Embeddings. CIKM 2025.
  • C. Ju, L. Collins, L. Neves, B. Kumar, L. Wang, T. Zhao, N. Shah. Generative Recommendation with Semantic IDs: A Practitioner’s Handbook. CIKM 2025. Best Paper Award.
  • Q. Truong, Z. Chen, C. Ju, T. Zhao, N. Shah, J. Tang. A Pre-Training Framework for Relational Data with Information Theoretic Principles. NeurIPS 2025.
  • D. Loveland, M. Ju, T. Zhao, N. Shah, D. Koutra. On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective. KDD 2025.
  • T. Zhao, Y. Liu, M. Kolodner, K. Montemayor, E. Ghazizadeh, et al. GiGL: Large-Scale Graph Neural Networks at Snapchat. KDD 2025.
  • M. Ju, L. Neves, B. Kumar, L. Collins, T. Zhao, et al. Revisiting Self-Attention for Cross-Domain Sequential Recommendation. KDD 2025.
  • M. Ju, L. Neves, B. Kumar, L. Collins, T. Zhao, et al. Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat. SIGIR 2025.
  • R. Xue, T. Zhao, N. Shah, X. Liu. Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks. ICML 2025.
  • N. Bui, M. Yang, R. Chen, L. Neves, C. Ju, R. Ying, N. Shah, T. Zhao. Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning. ICML 2025.
  • D. Loveland, X. Wu, T. Zhao, D. Koutra, N. Shah, M. Ju. Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. WWW 2025.
  • X. Wu, D. Loveland, R. Chen, Y. Liu, X. Chen, et al. GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. WWW 2025.
  • J. Zhu, Y. Zhou, S. Qian, Z. He, T. Zhao, N. Shah, D. Koutra. Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning. CVPR 2025.
  • J. Liu, H. Mao, Z. Chen, T. Zhao, N. Shah, J. Tang. Towards Neural Scaling Laws on Graphs. LoG 2024.
  • M. Ju, W. Shiao, Z. Guo, Y. Ye, Y. Liu, N. Shah, T. Zhao. Test-time Aggregation For Collaborative Filtering. NeurIPS 2024.
  • P. Kung, Z. Fan, T. Zhao, Y. Liu, Z. Lai, J. Shi, Y. Wu, J. Yu, N. Shah, G. Venkataraman. Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query Expansion. SIGIR 2024.
  • R. Chen, T. Zhao, A. K. Jaiswal, N. Shah, Z. Wang. LLaGA: Large Language and Graph Assistant. ICML 2024.
  • H. Mao, Z. Chen, W. Tang, J. Zhao, Y. Ma, T. Zhao, N. Shah, M. Galkin, J. Tang. Graph Foundation Models. ICML 2024.
  • A. Calabrese, L. Neves, N. Shah, M. Bos, B. Ross, M. Lapata, F. Barbieri. Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster. ACL 2024.
  • H. Mao, J. Li, H. Shomer, B. Li, W. Fan, Y. Ma, T. Zhao, N. Shah, J. Tang. Revisiting Link Prediction: A Data Perspective. ICLR 2024.
  • Y. Wang, T. Zhao, Y. Zhao, Y. Liu, X. Cheng, N. Shah, T. Derr. A Topological Perspective on Demystifying GNN-based Link Prediction Performance. ICLR 2024.
  • T. Zhao, N. Shah, E. Ghazizadeh. Learning from Graphs without Explicit Graph Machine Learning Models. ICLR 2024.
  • J. Li, H. Shomer, H. Mao, S. Zeng, Y. Ma, N. Shah, J. Tang, D. Yin. Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking. NeurIPS 2023.
  • H. Mao, Z. Chen, W. Jin, H. Han, Y. Ma, T. Zhao, N. Shah, J. Tang. Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?. NeurIPS 2023.
  • M. Ju, T. Zhao, W. Yu, N. Shah, F. Ye. GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Node Patching. NeurIPS 2023.
  • W. Shiao, U. S. Saini, Y. Liu, T. Zhao, N. Shah, E. Papalexakis. CARL-G: Clustering-Accelerated Representation Learning on Graphs. KDD 2023.
  • S. Bhatia, M. Wadhwa, K. Kawaguchi, N. Shah, P. S. Yu, B. Hooi. Sketch-based Anomaly Detection in Streaming Graphs. KDD 2023.
  • Z. Guo, W. Shiao, S. Zhang, Y. Liu, N. Chawla, N. Shah, T. Zhao. Linkless Link Prediction via Relational Distillation. ICML 2023.
  • J. Shi, V. Chaurasiya, Y. Liu, S. Vij, Y. Wu, S. Kanduri, N. Shah, P. Yu, N. Srivastava, L. Shi, G. Venkataraman, J. Yu. Embedding-based Retrieval in Friend Recommendation. SIGIR 2023.
  • J. Li, H. Shomer, J. Ding, Y. Wang, Y. Ma, N. Shah, J. Tang, D. Yin. Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?. ACL 2023.
  • X. Han, T. Zhao, Y. Liu, X. Hu, N. Shah. MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization. ICLR 2023.
  • M. Ju, T. Zhao, Q. Wen, W. Yu, N. Shah, Y. Ye, C. Zhang. Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization. ICLR 2023.
  • W. Shiao, Z. Guo, T. Zhao, V. Papalexakis, Y. Liu, N. Shah. Link Prediction with Non-Contrastive Learning. ICLR 2023.
  • W. Jin, T. Zhao, J. Ding, Y. Liu, J. Tang, N. Shah. Empowering Graph Representation Learning with Test-Time Graph Transformation. ICLR 2023.
  • Y. Wang, B. Hooi, Y. Liu, T. Zhao, Z. Guo, N. Shah. Flashlight: Scalable Link Prediction with Effective Decoders. LoG 2023.
  • Y. Wang, B. Hooi, Y. Liu, N. Shah. Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference. WSDM 2023.
  • R. Baten, Y. Liu, H. Peters, F. Barbieri, N. Shah, L. Neves, M. Bos. Predicting Future Location Categories of Users in a Large Social Platform. ICWSM 2023.
  • S. Zhang, Y. Liu, N. Shah, Y. Sun. Explaining Graph Neural Networks with Structure-Aware Cooperative Games. NeurIPS 2022.
  • L. Zhao, L. Härtel, N. Shah, L. Akoglu. A Practical, Progressively Expressive Graph Neural Network. NeurIPS 2022.
  • Y. Wang, Y. Zhao, N. Shah, T. Derr. Imbalanced Graph Classification via Graph-of-Graph Neural Networks. CIKM 2022.
  • J. Jiang, N. Murrugarra-Llerena, M. Bos, Y. Liu, N. Shah, L. Neves, F. Barbieri. Sunshine with a Chance of Smiles: How does Weather Impact Sentiment on Social Media?. ICWSM 2022.
  • S. Zhang, Y. Liu, Y. Sun, N. Shah. Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation. ICLR 2022.
  • W. Jin, L. Zhao, S. Zhang, Y. Liu, J. Tang, N. Shah. Graph Condensation for Graph Neural Networks. ICLR 2022.
  • L. Zhao, W. Jin, L. Akoglu, N. Shah. From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness. ICLR 2022.
  • Y. Ma, X. Liu, N. Shah, J. Tang. Is Homophily a Necessity for Graph Neural Networks?. ICLR 2022.
  • W. Jin, X. Liu, X. Zhao, Y. Ma, N. Shah, J. Tang. Automated Self-Supervised Learning for Graphs. ICLR 2022.
  • X. Tang, Y. Liu, X. He, S. Wang, N. Shah. Ranking Friend Stories on Social Platforms with Edge-Contextual Local Graph Convolutions. WSDM 2022.
  • S. Sikdar, N. Shah, T. Weninger. Attributed Graph Modeling with Vertex Replacement Grammars. WSDM 2022.
  • H. Shin, T. Kwon, N. Shah, K. Shin. Finding a Concise, Precise and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs. WSDM 2022.
  • T. Zhao, B. Ni, W. Yu, Z. Guo, N. Shah, M. Jiang. Action Sequence Augmentation for Early Graph-based Anomaly Detection. CIKM 2021.
  • Y. Ma, X. Liu, T. Zhao, Y. Liu, J. Tang, N. Shah. A Unified View on Graph Neural Networks as Graph Signal Denoising. CIKM 2021.
  • E. Gujral, L. Neves, E. Papalexakis, N. Shah. Niche Detection in User Content Consumption Data. CIKM 2021.
  • S. Shekhar, N. Shah, L. Akoglu. FairOD: Fairness-aware Outlier Detection. AIES 2021.
  • Q. Yang, W. Wang, L. Pierce, R. Vaish, X. Shi, N. Shah. Online Communication Shifts in the Midst of the Covid-19 Pandemic: A Case Study on Snapchat. ICWSM 2021.
  • F. A. Chowdhury, Y. Liu, K. Saha, N. Vincent, L. Neves, N. Shah, M. Bos. CEAM: The Effectiveness of Cyclic and Ephemeral Attention Models of User Behavior on Social Platforms. ICWSM 2021.
  • A. Sankar, Y. Liu, J. Yu, N. Shah. Graph Neural Networks for Friend Ranking in Large-scale Social Platforms. WWW 2021.
  • K. Saha, Y. Liu, N. Vincent, F. A. Chowdhury, L. Neves, N. Shah, M. Bos. AdverTiming Matters: Examining User Ad Consumption for Effective Ad Allocations on Social Media. CHI 2021.
  • T. Zhao, Y. Liu, L. Neves, O. Woodford, M. Jiang, N. Shah. Data Augmentation for Graph Neural Networks. AAAI 2021.
  • B. Joshi, F. Barbieri, N. Shah, L. Neves. The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks. COLING 2020.
  • P. Kaghazgaran, M. Bos, L. Neves, N. Shah. Social Factors in Closed-Network Content Consumption. CIKM 2020.
  • S. Abdali, R. Gurav, S. Menon, D. Fonseca, N. Entezari, N. Shah, E. Papalexakis. Identifying Misinformation from Website Screenshots. ICWSM 2021.
  • S. Abdali, N. Shah, E. Papalexakis. Semi-Supervised Multi-aspect Misinformation Detection with Hierarchical Joint Decomposition. ECML-PKDD 2020.
  • X. Tang, Y. Liu, N. Shah, X. Shi, P. Mitra, S. Wang. Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps. KDD 2020.
  • N. Shah. FARE: Schema-Agnostic Anomaly Detection in Social Event Logs. DSAA 2019.
  • H. Nilforoshan, N. Shah. SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs. DSAA 2019.
  • H. Lamba, N. Shah. Modeling Dwell Time Engagement on Visual Multimedia. KDD 2019.
  • H. Habib, N. Shah, R. Vaish. Impact of Contextual Factors on Public Snapchat Sharing. CHI 2019. Best Paper Honorable Mention Award.
  • S. Jain, D. Niranjan, H. Lamba, N. Shah, P. Kumaraguru. Characterizing and Detecting Livestreaming Chatbots. ASONAM 2019.
  • G. B. Guacho, S. Abdali, N. Shah, E. Papalexakis. Semi-Supervised Content-based Detection of Misinformation via Tensor Embeddings. ASONAM 2018.
  • N. Gupta, D. Eswaran, N. Shah, L. Akoglu, C. Faloutsos. Beyond Outlier Detection: LookOut for Pictorial Explanation. ECML-PKDD 2018.
  • N. Shah, H. Lamba, A. Beutel, C. Faloutsos. The Many Faces of Link Fraud. ICDM 2017.
  • D.-C. Juan, N. Shah, M. Tang, Z. Qian, D. Marculescu, C. Faloutsos. M3A: Model, MetaModel, and Anomaly Detection in Web Searches. DSAA 2017.
  • N. Shah. FLOCK: Combating Astroturfing on Livestreaming Platforms. WWW 2017.
  • B. Hooi, H. A. Song, A. Beutel, N. Shah, K. Shin, C. Faloutsos. FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. KDD 2016. Best Paper Award.
  • B. Hooi, N. Shah, A. Beutel, S. Günnemann, L. Akoglu, M. Kumar, D. Makhija, C. Faloutsos. BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. SDM 2016.
  • N. Shah, D. Koutra, T. Zou, B. Gallagher, C. Faloutsos. TimeCrunch: Interpretable Dynamic Graph Summarization. KDD 2015.
  • M. Giatsoglou, D. Chatzakou, N. Shah, A. Beutel, S. Guenneman, C. Faloutsos, A. Vakali. ND-Sync: Detecting Synchronized Fraud Activities. PAKDD 2015.
  • M. Giatsoglou, D. Chatzakou, N. Shah, C. Faloutsos, A. Vakali. Retweeting Activity on Twitter: Signs of Fraud. PAKDD 2015.
  • N. Shah, A. Beutel, B. Gallagher, C. Faloutsos. Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective. ICDM 2014.
  • N. Shah, E. Schendel, S. Pendse, S. Lakshminarasimhan, T. Rogers, N. Samatova. Improving I/O Throughput with PRIMACY: Preconditioning ID-Mapper for Compressing Incompressibility. CLUSTER 2012.
  • E. Schendel, Y. Jin, N. Shah, J. Chen, C.-S. Chang, S.-H. Ku, S. Ethier, S. Klasky, R. Latham, R. Ross, N. Samatova. ISOBAR Preconditioner for Effective and High-throughput Lossless Data Compression. ICDE 2012.
  • I. Arkatkar, J. Jenkins, S. Lakshminarasimhan, N. Shah, E. Schendel, S. Ethier, et al. Analytics-driven Lossless Data Compression for Rapid In-situ Indexing, Storing and Querying. DEXA 2012.
  • Y. Jin, S. Lakshminarasimhan, N. Shah, Z. Gong, C.-S. Chang, J. Chen, et al. S-preconditioner for Multi-fold Data Reduction with Guaranteed User-controlled Accuracy. ICDM 2011.
  • S. Lakshminarasimhan, N. Shah, S. Ethier, S. Klasky, R. Latham, R. Ross, N. Samatova. Compressing the Incompressible with ISABELA: In-situ Reduction of Spatio-Temporal Data. EuroPar 2011.
  • N. Shah, Y. Shpanskaya, C.-S. Chang, S.-H. Ku, A. Melechko, N. Samatova. Automatic and Statistically Robust Spatio-temporal Detection and Tracking of Fusion Plasma Fronts. SciDAC 2010.
  • P. Breimyer, G. Kora, W. Hendrix, N. Shah, N. Samatova. pR: Automatic Parallelization of Data-parallel Statistical Computing Codes for R in Hybrid Multi-node and Multi-core Environments. IADIS 2009.

Refereed Journal Publications

  • Z. Guo, T. Zhao, Y. Liu, K. Dong, W. Shiao, N. Shah, N. Chawla. Node Duplication Improves Cold-start Link Prediction. TMLR 2025.
  • Y. Dong, W. Shiao, Y. Liu, J. Li, N. Shah, T. Zhao. Do Graph Neural Networks Improve Node Representation Learning for All?. DMLR 2025.
  • F. Xia, R. Lambiotte, N. Shah, H. Tong, I. King. Guest Editorial: Special Issue on Graph Learning. IEEE TNNLS 2024.
  • D. Gomez-Zara, Y. Liu, L. Neves, N. Shah, M. Bos. Unpacking the Exploration–Exploitation Tradeoff on Snapchat. Computers in Human Behavior 2023.
  • T. Zhao, T. Jiang, N. Shah, M. Jiang. A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning. IEEE TNNLS 2021.
  • Y. Liu, T. Safavi, N. Shah, D. Koutra. Reducing Large Graphs to Small Supergraphs: A Unified Approach. SNAM 2018.
  • B. Hooi, K. Shin, H. A. Song, A. Beutel, N. Shah, C. Faloutsos. Graph-based Fraud Detection in the Face of Camouflage. TKDD 2017.
  • N. Shah, D. Koutra, L. Jin, T. Zou, B. Gallagher, C. Faloutsos. On Summarizing Large-Scale Dynamic Graphs. Data Engineering Bulletin 2017.
  • D. Koutra, N. Shah, J. T. Vogelstein, B. Gallagher, C. Faloutsos. DeltaCon: A Principled Massive-Graph Similarity Function with Attribution. TKDD 2015.
  • J. Jenkins, I. Arkatkar, S. Lakshminarasimhan, D. Boyuka, E. Schendel, N. Shah, et al. ALACRITY: Analytics-driven Lossless Data Compression for Rapid In-situ Indexing, Storing, and Querying. TLDKS 2013.
  • S. Lakshminarasimhan, N. Shah, S. Ethier, S. Klasky, R. Latham, R. Ross, N. Samatova. ISABELA for Effective In-situ Compression of Scientific Data. Concurrency and Computation: Practice and Experience 2011.

Refereed Workshop Publications

  • Improving Out-of-Vocabulary Handling in Recommendation Systems. TheWebConf RelWeb 2025.
  • M. Kolodner, M. Ju, Z. Fan, T. Zhao, E. Ghazizadeh, Y. Wu, N. Shah, Y. Liu. Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems. RecSys RobustRecSys 2024.
  • M. Ju, T. Zhao, W. Yu, N. Shah, Y. Ye. GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation. TheWebConf DCAI 2024.
  • P. Kung, Z. Fan, T. Zhao, Y. Liu, L. Lai, J. Shi, Y. Wu, N. Shah, J. Yu. Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query Expansion. TheWebConf DCAI 2024.
  • N. Shah. Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks. KDD MLG 2020.
  • R. Kumar, M. Kumar, N. Shah, C. Faloutsos. Did We Get It Right? Predicting Query Performance in E-commerce Search. SIGIR eCom 2018.
  • Y. Liu, T. Safavi, N. Shah, D. Koutra. Reducing Million-Node Graphs to a Few Structural Patterns: A Unified Approach. KDD MLG 2016.
  • N. Shah, A. Beutel, B. Hooi, L. Akoglu, S. Günnemann, D. Makhija, M. Kumar, C. Faloutsos. EdgeCentric: Anomaly Detection in Edge-Attributed Networks. ICDM DMCS 2016.
  • Y. Liu, N. Shah, D. Koutra. An Empirical Comparison of the Summarization Power of Graph Clustering Methods. NIPS NSIS 2015.

Surveys

  • H. Han, Y. Wang, H. Shomer, K. Guo, J. Ding, Y. Lei, M. Halappanavar, R. A. Rossi, et al. Retrieval-Augmented Generation with Graphs (GraphRAG). arXiv 2024.
  • T. Zhao, W. Jin, Y. Liu, Y. Wang, G. Liu, S. Günnemann, N. Shah, M. Jiang. Graph Data Augmentation for Graph Machine Learning. IEEE Data Engineering Bulletin 2023.
  • S. Kumar, N. Shah. False Information on the Web and Social Media. arXiv 2018.

Tutorials

  • Y. Liu, T. Zhao, M. Kolodner, K. Montemayor, S. Vij, N. Shah. Training Industry-Scale Graph Neural Networks with GiGL. KDD 2025.
  • R. Xue, H. Han, T. Zhao, N. Shah, J. Tang, X. Liu. Large-Scale Graph Neural Networks: The Past and New Frontiers. AAAI 2024.
  • R. Xue, H. Han, T. Zhao, N. Shah, J. Tang, X. Liu. Large-Scale Graph Neural Networks: The Past and New Frontiers. SDM 2024.
  • R. Xue, H. Han, T. Zhao, N. Shah, J. Tang, X. Liu. Large-Scale Graph Neural Networks: The Past and New Frontiers. KDD 2023.
  • T. Zhao, K. Ding, W. Jin, G. Liu, M. Jiang, N. Shah. Augmentation Methods for Graph Learning. SDM 2023.

Book Chapters

  • S. Abdali, G. Bastidas, N. Shah, E. Papalexakis. Tensor Embeddings for Content-Based Misinformation Detection with Limited Supervision. In: Disinformation, Misinformation, and Fake News in Social Media.
  • N. Shah. Introduction to R. In: Practical Graph Mining with R.
  • K. Padmanabhan, S. Lakshminarasimhan, Z. Gong, J. Jenkins, N. Shah, E. Schendel, I. Arkatkar, R. Ross, S. Klasky, N. Samatova. In-situ Analysis in Support of Exploratory Scientific Discovery in Data-Intensive Science. In: Data-Intensive Science.

Invited Talks

  • Panelist, AI-Augmented Academia: Research Innovations and Career Planning for Tomorrow’s Workforce, KDD Doctoral Consortium, 2025
  • Panelist, From Lists to Dialogues: Rethinking Personalization with Generative AI, KDD Workshop on Generative AI for Personalization, 2025
  • Panelist, From Research to Product, SIGIR Workshop on E-Commerce, 2025
  • Panelist, Is Search Dead? The Rise or Demise of Search in the Era of LLMs, SIGIR, 2025
  • Panelist, UCR exploreCSR Workshop, 2024
  • Keynote Speaker, KDD Undergraduate Consortium, 2024
  • Panelist, KDD Graph Learning Benchmarks (GLB) Workshop, 2023
  • Invited Speaker, Samsung Research, 2023
  • Keynote Speaker, RE-WORK AI Summit West, 2023
  • Panel Moderator, KDD Misinformation and Misbehavior (MIS²-TrueFact) Workshop, 2022
  • Panelist, KDD Deep Learning on Graphs (DLG) Workshop, 2022
  • Keynote Speaker, KDD Deep Learning on Graphs (DLG) Workshop, 2022
  • Panel Moderator, TigerGraph AI Summit, 2022
  • Invited Speaker, WSDM, 2022
  • Panelist, The Knowledge Graph Conference, 2022
  • Invited Speaker, The Knowledge Graph Conference, 2022
  • Invited Speaker, UC Riverside CSE Colloquium, 2022
  • Keynote Speaker, WSDM Machine Learning on Graphs (MLoG) Workshop, 2022
  • Panelist, UCR exploreCSR Workshop, 2022
  • Invited Speaker, Pinterest Trust and Safety Summit, 2021
  • Panel Moderator, KDD Outlier Detection and Discovery (ODD) Workshop, 2021
  • Keynote Speaker, KDD Machine Learning in Finance (MLF) Workshop, 2021
  • Keynote Speaker, SDM Doctoral Consortium, 2021
  • Keynote Speaker, SDM Minisymposium on Dynamic Networks, 2020
  • Keynote Speaker, ICDM Doctoral Consortium, 2019
  • Keynote Speaker, WWW CyberSafety Workshop, 2018
  • Keynote Speaker, KDD Outlier Detection De-constructed Workshop, 2018
  • Keynote Speaker, ECML-PKDD PhD Forum, 2018

Service — Conference Organization

  • Program Chair, CODS-COMAD, 2025
  • Sponsorship Chair, SDM, 2025
  • Hands-on Tutorial Chair, KDD, 2023, 2024
  • Sponsorship Chair, ICWSM, 2023
  • Cup Chair, WSDM, 2020, 2022
  • Program Chair, ASONAM, 2019
  • Organizer, KDD Generative AI for Recommender Systems and Personalization (GenAI4RecP) Workshop, 2025
  • Organizer, KDD Federated Learning with Graph Data (FedGraph) Workshop, 2024, 2025
  • Organizer, ICDM Mining and Learning on Graphs (MLoG) Workshop, 2022, 2023
  • Organizer, KDD Mining and Learning with Graphs (MLG) Workshop, 2022, 2023
  • Organizer, CIKM Federated Learning with Graph Data (FedGraph) Workshop, 2022
  • Organizer, KDD Misinformation and Misbehavior Mining (MIS²) Workshop, 2021, 2022
  • Organizer, TheWebConf CyberSafety Workshop, 2019, 2020
  • Session Chair, TheWebConf (Security and Privacy, 2018; Graph Models, 2021)
  • Session Chair, KDD (Graph Algorithms, 2020; Graphs and Networks, 2021; Graph Learning and Social Network, 2022)
  • Session Chair, DSAA (Subgraphs, 2019)
  • Session Chair, ICDM (Social track, 2016)

Service — Peer Review

  • Area Chair, NeurIPS, 2025
  • Area Chair, ICLR, 2025, 2026
  • Area Chair, KDD, 2022, 2025, 2026
  • Area Chair, TheWebConf, 2024, 2025, 2026
  • Area Chair, LoG, 2022, 2023, 2024, 2025
  • Senior Program Committee, AAAI, 2023, 2024, 2025, 2026
  • Senior Program Committee, PAKDD, 2023
  • Senior Program Committee, SDM, 2022–2025
  • Senior Program Committee, WSDM, 2022–2025
  • Senior Program Committee, CIKM, 2021–2025
  • Early Career Data Mining Award Committee, SDM, 2023, 2024
  • Best Paper Award Committee, SDM, 2023, 2024
  • Program Committee, ICLR, 2024
  • Program Committee, NeurIPS, 2023, 2024
  • Program Committee, ICDM, 2022
  • Program Committee, ASONAM, 2022
  • Program Committee, WSDM, 2019–2021
  • Program Committee, KDD, 2019–2021
  • Program Committee, TheWebConf, 2015, 2018, 2020–2022
  • Program Committee, SDM, 2018–2021
  • Program Committee, CIKM, 2017, 2020
  • Reviewer, ACM TKDD, 2018–2020; Springer DAMI, 2018, 2019; ACM TSOC, 2018, 2019; ACM TKDE, 2016, 2017
  • Reviewer, CSCW, 2019; CHI, 2019
  • Reviewer, WISE, 2014; IPDPS, 2011
  • Program Committee, numerous workshops (MLG, GLB, HeteroNAM, MIS², GreS, PhD Forums, Demo Sessions) at KDD, WSDM, WWW, ICML, ICDM, RecSys, CIKM

Mentorship — Internships and Student Advisory Roles

  • Jingzhe Liu (intern at Snap Research, 2025)
  • Geon Lee (intern at Snap Research, 2025)
  • Kulin Shah (intern at Snap Research, 2025)
  • Xingyue Huang (intern at Snap Research, 2025)
  • Xueying Ding (intern at Snap Research, 2025)
  • Ngoc Bui (intern at Snap Research, 2024)
  • Xinyi Wu (intern at Snap Research, 2024)
  • Donald Loveland (intern at Snap Research, 2024)
  • Runjin Chen (intern at Snap Research, 2024)
  • Jing Zhu (intern at Snap Research, 2024)
  • Haitao Mao (intern at Snap Research, 2024)
  • Agostina Calabrese (intern at Snap Research, 2023)
  • Yushun Dong (intern at Snap Research, 2023)
  • Vijay Prakash Dwivedi (intern at Snap Research, 2023)
  • Mingxuan Ju (intern at Snap Research, 2023)
  • Zhichun Guo (intern at Snap Research, 2022)
  • Vedant Bhatia (intern at Snap, 2022)
  • Yiwei Wang (intern at Snap Research, 2022)
  • William Shiao (intern at Snap Research, 2022 & 2023)
  • Xiaotian Han (intern at Snap Research, 2022)
  • Cazamere Comrie (intern at Snap Research, 2021)
  • Lingxiao Zhao (intern at Snap Research, 2021)
  • Wei Jin (intern at Snap Research, 2021 & 2022)
  • Shichang Zhang (intern at Snap Research, 2021)
  • Yingtong Dou (intern at Snap Research, 2021)
  • Yozen Liu (RA at Snap Research, 2020)
  • Qi Yang (intern at Snap Research, 2020)
  • Satyaki Sikdar (intern at Snap Research, 2020)
  • Yao Ma (intern at Snap Research, 2020)
  • Aravind Sankar (intern at Snap Research, 2020)
  • Tong Zhao (intern at Snap Research, 2020)
  • Nicholas Vincent (intern at Snap Research, 2020)
  • Farhan Asif Chowdhury (intern at Snap Research, 2020)
  • Koustuv Saha (intern at Snap Research, 2020)
  • Brihi Joshi (intern at Snap Research, 2019)
  • Shiyan Yan (intern at Snap Research, 2019)
  • Xianfeng Tang (intern at Snap Research, 2019 & 2020)
  • Parisa Kaghazgaran (intern at Snap Research, 2019)
  • Himel Dev (intern at Snap Research, 2019)
  • Anis Zaman (intern at Snap Research, 2019)
  • Can Liu (intern at Snap Research, 2019)
  • Dipankar Niranjan (BS student, IIIT Delhi, 2018)
  • Shreya Jain (BS student, IIIT Delhi, 2018)
  • Hamed Nilforoshan (intern at Snap Research, 2018)
  • Hana Habib (intern at Snap Research, 2018)
  • Hemank Lamba (intern at Snap Research, 2018)
  • Rohan Kumar (visiting CS student at CMU, 2017)
  • Qicheng Huang (EE PhD student at CMU, 2017)
  • Chenlei Fang (EE PhD student at CMU, 2017)
  • Tianmin Zou (CS MS student at CMU, 2017)

Mentorship — Thesis Supervision

  • Committee Member, Haitao Mao, 2024
  • Committee Member, Harry Shomer, 2024
  • Committee Member, Lingxiao Zhao, 2024
  • Committee Member, William Shiao, 2024
  • Committee Member, Zhichun Guo, 2023
  • Committee Member, Wei Jin, 2023
  • Committee Member, Yingtong Dou, 2021
  • Committee Member, Aravind Sankar, 2021
  • Committee Member, Tong Zhao, 2020

Funding

  • Contributed towards Flipkart faculty grant (with CMU: Bryan Hooi, Dhivya Eswaran, Christos Faloutsos)
  • Contributed towards Wharton Customer Analytics Initiative proposal Fraud Detection through Mining Dynamic Behavior for Group Anomalies (with CMU: Alex Beutel, Christos Faloutsos)
  • Contributed towards PNC Center for Financial Services proposal PF15003: Fraud Detection in Financial Data (with CMU: Alex Beutel, Christos Faloutsos)
  • Contributed towards DOE-NNSA-30788.1.1990222: Quantifying Network Changes (with CMU: Danai Koutra, Christos Faloutsos)
  • Contributed towards NSF IIS-1028746: Collaborative Research: Understanding Climate Change — A Data Driven Approach (with NCSU: Nagiza Samatova, Fredrick Semazzi)

Teaching

  • Guest Lecture, Improving the Scalability of Graph Neural Networks, CMU 11-741 Machine Learning for Text and Graph-based Mining (Prof. Yiming Yang), 2024
  • Seminar Talk, Improving the Scalability of Graph Neural Networks, Brandeis University Machine Learning Seminar, 2024
  • Guest Lecture, Improving the Scalability of Graph Neural Networks, Georgia Tech ISYE 4803 Network Science and Analysis (Prof. Tejas Santanam), 2023
  • Guest Lecture, Improving the Scalability of Graph Neural Networks, Georgia Tech CSE 6240 Web Search and Text Mining (Prof. Srijan Kumar), 2023
  • Guest Lecture, Improving the Scalability of Graph Neural Networks, Michigan State CSE 482 Big Data Analysis (Prof. Jiliang Tang), 2022
  • Guest Lecture, Machine Learning on Graphs with Scarce Labels, RPI MGMT-6560-02 Introduction to Machine Learning Applications (Prof. Lydia Manikonda), 2021
  • Guest Lecture, Mining Misbehavior on Large-Scale Social Platforms, Vanderbilt CS-5981-06 Social Network Analysis (Prof. Tyler Derr), 2020
  • Guest Lecture, A Foray into Graph Mining, USC CSCI-699 Introduction to Information Extraction (Prof. Xiang Ren), 2019
  • Guest Lecture, Graph Mining for Fraud Detection, CMU 15-300 Research and Innovation in Computer Science (Prof. Todd Mowry), 2015
  • Teaching Assistant, CMU 15-300 Research and Innovation in Computer Science (Prof. Todd Mowry), 2015
  • Teaching Assistant, CMU 15-826 Multimedia Databases and Data Mining (Prof. Christos Faloutsos), 2014

Skills

Cloud Platforms: GCP, AWS
Languages/Tools: Python, Java, C, C++, x86 Assembly
Web: SQL, HTML, PHP, JavaScript, CSS, WordPress
Engineering Tools: MATLAB, R
Typesetting: LaTeX, Microsoft Office, LibreOffice
Version Control: Git, Subversion
Operating Systems: macOS, Ubuntu Linux, Microsoft Windows