Contact
- neil at nshah dot net [personal]
- nshah at snap dot com [work]
Location
- 110 110th Ave NE
- Bellevue, WA 98004
About Me
I am currently a research scientist and manager at Snap Research, leading a team of scientists, engineers and interns on fundamental and applied research initiatives in user modeling and personalization across Snapchat. I am especially interested in advancing the state-of-the-art in machine learning algorithms and applications on large-scale structured data, especially in regards to user-centric applications like ranking, recommendation systems, integrity and more. My work broadly spans the machine learning, data mining, and software engineering domains.
Before I joined Snap, I defended my PhD thesis in the Computer Science Department at Carnegie Mellon University, where I worked on discovering and modeling various types of abusive online behaviors in large networks. I was very fortunate to have been advised by Christos Faloutsos.
Prior to this, I received my B.S. in Computer Science from the Department of Computer Science at North Carolina State University. There, I worked with Nagiza Samatova on reduction, indexing and storage systems for large-scale scientific data.
If you would like to learn more about my work, please take a look at my publications and CV. Please reach out to me via my personal e-mail for consulting inquiries.
Recent News
- Sep 2024: Our work on test-time message passing for collaborative filtering was accepted to NeurIPS 2024 -- see you in Vancouver!
- Aug 2024: Check our some interesting preprints on neural scaling laws with graph models and the first multimodal graph benchmark!
- July 2024: Check out a preprint of NodeDup, a super simple approach for drastically improving graph link prediction performance with data augmentation!
- May 2024: Our work on language models for content moderation was accepted to ACL 2024 -- see you in Bangkok!
- April 2024: We have multiple papers at ICML 2024 on exploring graph foundation models and adapting language models for graph tasks -- see you in Vienna!
- March 2024: We'll present an iteration of our popular large-scale graph neural networks tutorial at SDM 2024 -- see you in Houston!
- February 2024: Excited to give a tutorial at AAAI 2024 on large-scale graph neural networks -- see you in Vancouver!
- February 2024: We will present multiple works at ICLR 2024 on graph neural networks, specifically around demystifying link prediction performance and conceptualizing graph learning without explicit graph models -- see you in Vienna!
- October 2023: We have multiple papers at NeurIPS 2023, on cold-start graph learning, evaluation pitfalls in link prediction and graph structural disparity -- see you in New Orleans!
- August 2023: Excited to be deeply involved at KDD 2023, participating in panels, workshops, tutorials and paper presentations; come say hello in Long Beach!
- August 2023: Honored that our work Graph Data Augmentation for Graph Machine Learning: A Survey was featured in the IEEE Data Engineering Bulletin edition on Graph Neural Networks.
- July 2023: It's a busy week for our team at Snap! Several of us will be at SIGIR 2023, presenting our work on Embedding-based Retrieval in Friend Recommendation, and several others will be at ICML 2023, presenting our work on Linkless Link Prediction via Relational Distillation.
- June 2023: We have several papers at KDD 2023, on graph anomaly detection, clustering-based graph self-supervised-learning, and scaling up graph neural networks -- see you in Long Beach!
- May 2023: Our paper Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? was accepted to ACL 2023 -- see you in Toronto!
- May 2023: Our paper Linkless Link Prediction via Relational Distillation was accepted to ICML 2023 -- see you in Honolulu!
- Apr 2023: Our tutorial Large-Scale Graph Neural Networks: The Past and New Frontiers was accepted to KDD 2023 -- see you in Long Beach!
- Mar 2023: Our paper Embedding-based Retrieval in Friend Recommendation was accepted to SIRIP 2023 -- see you in Taipei!
- Feb 2023: Our tutorial Augmentation Methods for Graph Learning was accepted to SDM 2023 -- see you in Minneapolis!
- Jan 2023: Our paper MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization was accepted to ICLR 2023 -- see you in Kigali!
- Jan 2023: Our paper Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization was accepted to ICLR 2023 -- see you in Kigali!
- Jan 2023: Our paper Link Prediction with Non-Contrastive Learning was accepted to ICLR 2023 -- see you in Kigali!
- Jan 2023: Our paper Empowering Graph Representation Learning with Test-Time Graph Transformation was accepted to ICLR 2023 -- see you in Kigali!
- Nov 2022: Our paper Flashlight: Scalable Link Prediction with Effective Decoders was accepted to LoG 2023 -- see you over Zoom!
- Nov 2022: Our paper Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference was accepted to WSDM 2023 -- see you in Singapore!
- Oct 2022: Excited to serve as a panelist at the Stanford Graph Learning Workshop 2022 -- see you in Stanford, CA!
- Sep 2022: Our paper Explaining Graph Neural Networks with Structure-Aware Cooperative Games was accepted to NeurIPS 2022 -- see you in New Orleans!
- Sep 2022: Our paper A Practical, Progressively Expressive Graph Neural Network was accepted to NeurIPS 2022 -- see you in New Orleans!
- Aug 2022: Our paper Imbalanced Graph Classification via Graph-of-Graph Neural Networks was accepted to CIKM 2022 -- see you in Atlanta!
- Aug 2022: Honored to give a keynote and serve on a panel at the KDD 2022 Deep Learning on Graphs (DLG) Workshop!
- Aug 2022: Excited to co-organize the KDD 2022 Mining and Learning with Graphs (MLG) Workshop!
- July 2022: Our paper Predicting Future Location Categories of Users in a Large Social Platform was accepted to ICWSM 2023 -- see you in Cyprus!
- May 2022: Excited to moderate a panel at the TigerGraph AI Summit on graph deep learning libraries!
- May 2022: Honored to serve as an invited speaker and panelist at the Knowledge Graph Conference 2022, where I'll talk about advances in industrial-scale graph ML!
- Apr 2022: Looking forward to giving an invited talk at the UC Riverside CSE Colloqium on our work about Scaling Up Graph Neural Networks at Snap!
- Mar 2022: Honored to receive the Outstanding Service Award at WSDM 2022!
- Jan 2022: Our paper Sunshine with a Chance of Smiles: How does Weather Impact Sentiment on Social Media? was accepted to ICWSM 2022 -- see you over Zoom!
- Feb 2022: Excited to organize WSDM Cup 2022 on Feb 25th, and see theory meet practice across tasks like user retention prediction, temporal link prediction and cross-market recommendation!
- Feb 2022: Excited to give a keynote at the WSDM Machine Learning on Graphs (MLoG) Workshop and talk about some of our recent advances in scaling up graph neural network training and inference at Snap!
- Jan 2022: Our paper Graph-less Neural Networks: Teaching Old MLPs new Tricks via Distillation was accepted to ICLR 2022 -- see you over Zoom!
- Jan 2022: Our paper Graph Condensation for Graph Neural Networks was accepted to ICLR 2022 -- see you over Zoom!
- Jan 2022: Our paper From Stars to Subgraphs: Uplifting any GNN with Local Structure Awareness was accepted to ICLR 2022 -- see you over Zoom!
- Jan 2022: Our paper Is Homophily a Necessity for Graph Neural Networks? was accepted to ICLR 2022 -- see you over Zoom!
- Jan 2022: Our paper Automated Self-Supervised Learning for Graphs was accepted to ICLR 2022 -- see you over Zoom!
- Oct 2021: Excited to talk about some of our recent advances in scarce-label graph learning at the IIIT Delhi Laboratory for Computational Social Systems Seminar Series as an invited speaker.
- Oct 2021: Our paper Ranking Friend Stories on Social Platforms with Edge-Contextual Local Graph Convolutions was accepted to WSDM 2022 -- see you in Phoenix!
- Oct 2021: Our paper Attributed Graph Modeling with Vertex Replacement Grammars was accepted to WSDM 2022 -- see you in Phoenix!
- Oct 2021: Our paper Finding a Concise, Precise and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs was accepted to WSDM 2022 -- see you in Phoenix!
- Aug 2021: Our paper Action Sequence Augmentation for Early Graph-based Anomaly Detection was accepted to CIKM 2021 -- see you over Zoom!
- Aug 2021: Our paper A Unified View on Graph Neural Networks as Graph Signal Denoising was accepted to CIKM 2021 -- see you over Zoom!
- Aug 2021: Our paper Niche Detection in User Content Consumption Data was accepted to CIKM 2021 -- see you over Zoom!
- Aug 2021: Our paper A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning was accepted to TNNLS 2021!
- Jun 2021: I will be co-organizing WSDM Cup at WSDM 2022 -- the call for task proposals is out; looking forward to your amazing submissions!
- Apr 2021: Our paper FairOD: Fairness-aware Outlier Detection was accepted to AIES 2021 -- see you over Zoom!
- Mar 2021: I will be co-organizing the Misinformation and Misbehavior (MIS2) workshop at KDD 2021 this year -- see you over Zoom!
- Mar 2021: I will be a keynote speaker at the ML in Finance (MLF) workshop at KDD 2021 this year -- see you over Zoom!
- Mar 2021: Our paper Online Communication Shifts in the Midst of the Covid-19 Pandemic: A Case Study on Snapchat was accepted to ICWSM 2021 -- see you (hopefully) in Atlanta, GA!
- Mar 2021: Our paper CEAM: The Effectiveness of Cyclic and Ephemeral Attention Models of User Behavior on Social Platforms was accepted to ICWSM 2021 -- see you (hopefully) in Atlanta, GA!
- Dec 2020: Our paper AdverTiming Matters: Examining User Ad Consumption for Effective Ad Allocations on Social Media was accepted to CHI 2021 -- see you over Zoom!
- Dec 2020: Our exciting new work FairOD: Fairness-aware Outlier Detection is now available as a preprint!
- Dec 2020: Our paper Data Augmentation for Graph Neural Networks was accepted to AAAI 2021 -- see you over Zoom!
- Oct 2020: Our exciting new work A Unified View on Graph Neural Networks as Graph Signal Denoising is now available as as preprint!
- Sep 2020: Our paper The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks was accepted to COLING 2020 -- see you over Zoom!
- Jul 2020: Our paper Social Factors in Closed-Network Content Consumption was accepted to CIKM 2020 -- see you over Zoom!
- Jul 2020: Our paper Identifying Misinformation from Website Screenshots was accepted to ICWSM 2021 -- see you (hopefully) in Venice!
- Jul 2020: Our paper Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks was accepted to KDD MLG 2020 -- see you over Zoom!
- Jun 2020: Our exciting new work Data Augmentation for Graph Neural Networks is now available as a preprint!
- Jun 2020: Our paper Semi-Supervised Multi-aspect Misinformation Detection with Hierarchical Joint Decomposition was accepted to ECML-PKDD 2020 -- see you over Zoom!
- May 2020: Our paper Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps was accepted to KDD 2020 -- see you over Zoom!
- Feb 2020: I will be chairing the CyberSafety workshop at TheWebConf 2020 -- see you over Zoom!
- Oct 2019: I will be a keynote speaker and panelist at the Doctoral Consortium at ICDM 2019 -- see you in Beijing!
- Jul 2019: I will be giving a keynote talk on Outlier Detection for Mining Social Misbehavior at the Learning and Mining for Cybersecurity (LEMINCS) workshop at KDD 2019 -- see you in Anchorage!
- Jul 2019: Our paper SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs was accepted to DSAA 2019 -- see you in Washington, DC!
- Jul 2019: Our paper FARE: Schema-Agnostic Anomaly Detection in Social Event Logs was accepted to DSAA 2019 -- see you in Washington, DC!
- Jul 2019: I will be chairing WSDM Cup at WSDM 2020 -- see you in Houston!
- Jun 2019: Our paper Characterizing and Detecting Livestreaming Chatbots was accepted to ASONAM 2019 -- see you in Vancouver!
- Apr 2019: Our paper Modeling Dwell Time Engagement on Visual Multimedia was accepted to KDD 2019 -- see you in Anchorage!
- Mar 2019: Our CHI 2019 paper on Impact
of Contextual Factors on Public Snapchat Sharing
won a Best Paper Honorable Mention award! - Dec 2018: Our paper Impact of
Contextual Factors on Public Snapchat Sharing
was accepted to CHI 2019 -- see you in Glasgow! - Dec 2018: Our work on false information with Vagelis Papalexakis was featured on the NVIDIA AI Podcast!
- Nov 2018: I will be chairing the CyberSafety workshop at WWW 2019 -- see you in San Francisco!
- Oct 2018: I will be chairing the ASONAM 2019 Industrial Track -- see you in Vancouver!
- Sep 2018: I will be giving a keynote talk on Lessons I learned during my PhD at the PhD Forum at ECML-PKDD 2019 -- see you in Dublin!
- Aug 2018: Our work on false information with Vagelis Papalexakis is featured in a Digital Trends article.
- Jul 2018: I will be giving a keynote talk on Outlier Detection for Mining Social Misbehavior at the Outlier Detection De-constructed (ODD) workshop at KDD 2018 -- see you in London!
- Jun 2018: Our paper Did We Get It Right? Predicting Query Performance in E-commerce Search was accepted to the eCom Workshop at SIGIR 2018!
- Jun 2018: Our paper Beyond Outlier Detection: LookOut for Pictorial Explanation was accepted to ECML-PKDD 2018 -- see you in Dublin!
- Jun 2018: Our paper Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings was accepted to ASONAM 2018 -- see you in Barcelona!
- Apr 2018: Srijan Kumar and I published a comprehensive survey on characterizing and detecting false information, False Information on Web and Social Media: A Survey. Check it out!
- Mar 2018: I will be giving a keynote talk on Anomaly Detection on Large Social Graphs at the CyberSafety workshop at WWW 2018 -- see you in Lyon!
- Jan 2018: Our paper Reducing Large Graphs to Small Supergraphs: A Unified Approach will be published in SNAM 2018!
- Dec 2017: I have joined Snap Inc as a Research Scientist in Los Angeles, CA!
- Oct 2017: I successfully defended my thesis, Anomaly Detection in Large Social Graphs, from Carnegie Mellon University's School of Computer Science, advised by Christos Faloutsos.