Heralding New Ringtones of Patient Safety: Blackberry-based Clinical Communication and Telementoring in Laparoscopic Surgery. - Ogochukwu Azuh; Ayan Sen; Rubinfeld Ilan; Neil Shah; Alton Parker; Dionne Blyden; Autumn Broady; Vic Velanovich; Anthony Falvo; Pat Patton (AMIA, 2009)

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 AC (2), 2009)

Compressing the Incompressible with ISABELA: In-situ Reduction of Spatio-temporal Data. - Sriram Lakshminarasimhan; Neil Shah; Stéphane Ethier; Scott Klasky; Robert Latham; Robert B. Ross; Nagiza F. Samatova (Euro-Par (1), 2011)

S-preconditioner for Multi-fold Data Reduction with Guaranteed User-Controlled Accuracy. - Ye Jin; Sriram Lakshminarasimhan; Neil Shah; Zhenhuan Gong; Choong-Seock Chang; Jackie Chen; Stéphane Ethier; Hemanth Kolla; Seung-Hoe Ku; Scott Klasky; Robert Latham; Robert B. Ross; et al. (ICDM, 2011)

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 (CLUSTER, 2012)

Analytics-Driven Lossless Data Compression for Rapid In-situ Indexing, Storing, and Querying. - John Jenkins; Isha Arkatkar; Sriram Lakshminarasimhan; Neil Shah; Eric R. Schendel; Stéphane Ethier; Choong-Seock Chang; Jacqueline H. Chen; Hemanth Kolla; Scott Klasky; Robert B. Ross; Nagiza F. Samatova (DEXA (2), 2012)

ISOBAR Preconditioner for Effective and High-throughput Lossless Data Compression. - Eric R. Schendel; Ye Jin; Neil Shah; Jackie Chen; Choong-Seock Chang; Seung-Hoe Ku; Stéphane Ethier; Scott Klasky; Robert Latham; Robert B. Ross; Nagiza F. Samatova (ICDE, 2012)

Challenges in understanding clinical notes: why NLP engines fall short and where background knowledge can help. - Sujan Perera; Amit P. Sheth; Krishnaprasad Thirunarayan; Suhas Nair; Neil Shah (DARE@CIKM, 2013)

ISABELA for effective in situ compression of scientific data. - Sriram Lakshminarasimhan; Neil Shah; Stéphane Ethier; Seung-Hoe Ku; Choong-Seock Chang; Scott Klasky; Robert Latham; Robert B. Ross; Nagiza F. Samatova (Concurr. Comput. Pract. Exp., 2013)

ALACRITY: Analytics-Driven Lossless Data Compression for Rapid In-Situ Indexing, Storing, and Querying. - John Jenkins; Isha Arkatkar; Sriram Lakshminarasimhan; David A. Boyuka II; Eric R. Schendel; Neil Shah; Stéphane Ethier; Choong-Seock Chang; Jackie Chen; Hemanth Kolla; Scott Klasky; Robert B. Ross; et al. (Trans. Large Scale Data Knowl. Centered Syst., 2013)

Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective. - Neil Shah; Alex Beutel; Brian Gallagher; Christos Faloutsos (ICDM, 2014)

TimeCrunch: Interpretable Dynamic Graph Summarization. - Neil Shah; Danai Koutra; Tianmin Zou; Brian Gallagher; Christos Faloutsos (KDD, 2015)

ND-Sync: Detecting Synchronized Fraud Activities. - Maria Giatsoglou; Despoina Chatzakou; Neil Shah; Alex Beutel; Christos Faloutsos; Athena Vakali (PAKDD (2), 2015)

Retweeting Activity on Twitter: Signs of Deception. - Maria Giatsoglou; Despoina Chatzakou; Neil Shah; Christos Faloutsos; Athena Vakali (PAKDD (1), 2015)

An Empirical Comparison of the Summarization Power of Graph Clustering Methods. - Yike Liu; Neil Shah; Danai Koutra (CoRR, 2015)

S-index: Towards Better Metrics for Quantifying Research Impact. - Neil Shah; Yang Song (CoRR, 2015)

EdgeCentric: Anomaly Detection in Edge-Attributed Networks. - Neil Shah; Alex Beutel; Bryan Hooi; Leman Akoglu; Stephan Günnemann; Disha Makhija; Mohit Kumar 008; Christos Faloutsos (ICDM Workshops, 2016)

FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. - Bryan Hooi; Hyun Ah Song; Alex Beutel; Neil Shah; Kijung Shin; Christos Faloutsos (KDD, 2016)

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. - Bryan Hooi; Neil Shah; Alex Beutel; Stephan Günnemann; Leman Akoglu; Mohit Kumar 008; Disha Makhija; Christos Faloutsos (SDM, 2016)

M3A: Model, MetaModel, and Anomaly Detection in Web Searches. - Da-Cheng Juan; Neil Shah; Mingyu Tang; Zhiliang Qian; Diana Marculescu; Christos Faloutsos (CoRR, 2016)

DeltaCon: Principled Massive-Graph Similarity Function with Attribution. - Danai Koutra; Neil Shah; Joshua T. Vogelstein; Brian Gallagher; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2016)

M3A: Model, MetaModel and Anomaly Detection for Inter-arrivals of Web Searches and Postings. - Da-Cheng Juan; Neil Shah; Mingyu Tang; Zhiliang Qian; Diana Marculescu; Christos Faloutsos (DSAA, 2017)

The Many Faces of Link Fraud. - Neil Shah; Hemank Lamba; Alex Beutel; Christos Faloutsos (ICDM, 2017)

FLOCK: Combating Astroturfing on Livestreaming Platforms. - Neil Shah (WWW, 2017)

OEC: Open-Ended Classification for Future-Proof Link-Fraud Detection. - Neil Shah; Hemank Lamba; Alex Beutel; Christos Faloutsos (CoRR, 2017)

LookOut on Time-Evolving Graphs: Succinctly Explaining Anomalies from Any Detector. - Nikhil Gupta 007; Dhivya Eswaran; Neil Shah; Leman Akoglu; Christos Faloutsos (CoRR, 2017)

On Summarizing Large-Scale Dynamic Graphs. - Neil Shah; Danai Koutra; Lisa Jin; Tianmin Zou; Brian Gallagher; Christos Faloutsos (IEEE Data Eng. Bull., 2017)

Graph-Based Fraud Detection in the Face of Camouflage. - Bryan Hooi; Kijung Shin; Hyun Ah Song; Alex Beutel; Neil Shah; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2017)

Time-Frequency Mask-based Speech Enhancement using Convolutional Generative Adversarial Network. - Neil Shah; Hemant A. Patil; Meet H. Soni (APSIPA, 2018)

Novel Inter Mixture Weighted GMM Posteriorgram for DNN and GAN-based Voice Conversion. - Nirmesh J. Shah; R. Sreeraj; Neil Shah; Hemant A. Patil (APSIPA, 2018)

Novel Spectral Root Cepstral Features for Replay Spoof Detection. - Prasad A. Tapkir; Ankur T. Patil; Neil Shah; Hemant A. Patil (APSIPA, 2018)

Semi-supervised Content-Based Detection of Misinformation via Tensor Embeddings. - Gisel Bastidas Guacho; Sara Abdali; Neil Shah; Evangelos E. Papalexakis (ASONAM, 2018)

Time-Frequency Masking-Based Speech Enhancement Using Generative Adversarial Network. - Meet H. Soni; Neil Shah; Hemant A. Patil (ICASSP, 2018)

Effectiveness of Generative Adversarial Network for Non-Audible Murmur-to-Whisper Speech Conversion. - Neil Shah; Nirmesh J. Shah; Hemant A. Patil (INTERSPEECH, 2018)

Beyond Outlier Detection: LookOut for Pictorial Explanation. - Nikhil Gupta 007; Dhivya Eswaran; Neil Shah; Leman Akoglu; Christos Faloutsos (ECML/PKDD (1), 2018)

Did We Get It Right? Predicting Query Performance in e-Commerce Search. - Rohan Kumar; Mohit Kumar 008; Neil Shah; Christos Faloutsos (eCOM@SIGIR, 2018)

False Information on Web and Social Media: A Survey. - Srijan Kumar; Neil Shah (CoRR, 2018)

Reducing large graphs to small supergraphs: a unified approach. - Yike Liu; Tara Safavi; Neil Shah; Danai Koutra (Soc. Netw. Anal. Min., 2018)

Characterizing and detecting livestreaming chatbots. - Shreya Jain; Dipankar Niranjan; Hemank Lamba; Neil Shah; Ponnurangam Kumaraguru (ASONAM, 2019)

Impact of Contextual Factors on Snapchat Public Sharing. - Hana Habib; Neil Shah; Rajan Vaish (CHI, 2019)

SliceNDice: Mining Suspicious Multi-Attribute Entity Groups with Multi-View Graphs. - Hamed Nilforoshan; Neil Shah (DSAA, 2019)

FARE: Schema-Agnostic Anomaly Detection in Social Event Logs. - Neil Shah (DSAA, 2019)

Modeling Dwell Time Engagement on Visual Multimedia. - Hemank Lamba; Neil Shah (KDD, 2019)

Cybersafety 2019: The 4th Workshop on Computational Methods in Online Misbehavior. - Richard Han 001; Neil Shah (WWW (Companion Volume), 2019)

Thrombophilia testing in the inpatient setting: impact of an educational intervention. - Henry Kwang; Eric Mou; Ilana Richman; Andre Kumar; Caroline Berube; Rajani Kaimal; Neera Ahuja; Stephanie Harman; Tyler Johnson; Neil Shah; Ronald Witteles; Robert Harrington; et al. (BMC Medical Informatics Decis. Mak., 2019)

Minority Reports Defense: Defending Against Adversarial Patches. - Michael McCoyd; Won Park; Steven Chen; Neil Shah; Ryan Roggenkemper; Minjune Hwang; Jason Xinyu Liu; David A. Wagner 001 (ACNS Workshops, 2020)

Impact of Minimum Hyperspherical Energy Regularization on Time-Frequency Domain Networks for Singing Voice Separation. - Neil Shah; Dharmeshkumar M. Agrawal (APSIPA, 2020)

Query-By-Example Spoken Term Detection Using Generative Adversarial Network. - Neil Shah; Sreeraj R; Maulik C. Madhavi; Nirmesh J. Shah; Hemant A. Patil (APSIPA, 2020)

Social Factors in Closed-Network Content Consumption. - Parisa Kaghazgaran; Maarten W. Bos; Leonardo Neves; Neil Shah (CIKM, 2020)

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

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; Suhang Wang (KDD, 2020)

Semi-supervised Multi-aspect Detection of Misinformation Using Hierarchical Joint Decomposition. - Sara Abdali; Neil Shah; Evangelos E. Papalexakis (ECML/PKDD (5), 2020)

HiJoD: Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition. - Sara Abdali; Neil Shah; Evangelos E. Papalexakis (CoRR, 2020)

A Large-Scale Analysis of Attacker Activity in Compromised Enterprise Accounts. - Neil Shah; Grant Ho; Marco Schweighauser; M. H. Afifi; Asaf Cidon; David A. Wagner 001 (CoRR, 2020)

Whole-genome sequencing of patients with rare diseases in a national health system. - Ernest Turro; William J. Astle; Karyn Megy; Stefan Gräf; Daniel Greene; Olga Shamardina; Hana Lango Allen; Alba Sanchis-Juan; Mattia Frontini; Chantal Thys; Jonathan Stephens; Rutendo Mapeta; et al. (Nat., 2020)

Data Augmentation for Graph Neural Networks. - Tong Zhao 003; Yozen Liu; Leonardo Neves; Oliver J. Woodford; Meng Jiang 001; Neil Shah (AAAI, 2021)

FairOD: Fairness-aware Outlier Detection. - Shubhranshu Shekhar; Neil Shah; Leman Akoglu (AIES, 2021)

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 W. Bos (CHI, 2021)

A Unified View on Graph Neural Networks as Graph Signal Denoising. - Yao Ma 001; Xiaorui Liu; Tong Zhao 003; Yozen Liu; Jiliang Tang; Neil Shah (CIKM, 2021)

Action Sequence Augmentation for Early Graph-based Anomaly Detection. - Tong Zhao 003; Bo Ni; Wenhao Yu 002; Zhichun Guo; Neil Shah; Meng Jiang 001 (CIKM, 2021)

NED: Niche Detection in User Content Consumption Data. - Ekta Gujral; Leonardo Neves; Evangelos E. Papalexakis; Neil Shah (CIKM, 2021)

Identifying Misinformation from Website Screenshots. - Sara Abdali; Rutuja Gurav; Siddharth Menon; Daniel Fonseca; Negin Entezari; Neil Shah; Evangelos E. Papalexakis (ICWSM, 2021)

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 W. Bos (ICWSM, 2021)

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 (ICWSM, 2021)

The Second International MIS2 Workshop: Misinformation and Misbehavior Mining on the Web. - Aude Hofleitner; Meng Jiang 001; Srijan Kumar; Neil Shah; Kai Shu (KDD, 2021)

Graph Neural Networks for Friend Ranking in Large-scale Social Platforms. - Aravind Sankar; Yozen Liu; Jun Yu; Neil Shah (WWW, 2021)

KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection. - Sara Abdali; Neil Shah; Evangelos E. Papalexakis (CoRR, 2021)

Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention. - Neil Shah; Nandish Bhagat; Manan Shah (Vis. Comput. Ind. Biomed. Art, 2021)

Extending the Standard REDCap Dictionary Definitions Using REDCap. - Andrew Niederhausern; Nadia Bahadur; Neil Shah; Fenil Gandhi; Alysha Insinga; Kimberly Chan; Laura Barton; John Philip; Chelsea Nichols (AMIA, 2022)

Imbalanced Graph Classification via Graph-of-Graph Neural Networks. - Yu Wang 160; Yuying Zhao; Neil Shah; Tyler Derr (CIKM, 2022)

The 1st International Workshop on Federated Learning with Graph Data (FedGraph). - Carl Yang 001; Xiaoxiao Li; Nathalie Baracaldo; Neil Shah; Chaoyang He 001; Lingjuan Lyu; Lichao Sun 001; Salman Avestimehr (CIKM, 2022)

Is Homophily a Necessity for Graph Neural Networks? - Yao Ma 001; Xiaorui Liu; Neil Shah; Jiliang Tang (ICLR, 2022)

Automated Self-Supervised Learning for Graphs. - Wei Jin 009; Xiaorui Liu; Xiangyu Zhao 001; Yao Ma 001; Neil Shah; Jiliang Tang (ICLR, 2022)

Graph Condensation for Graph Neural Networks. - Wei Jin 009; Lingxiao Zhao; Shichang Zhang; Yozen Liu; Jiliang Tang; Neil Shah (ICLR, 2022)

Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation. - Shichang Zhang; Yozen Liu; Yizhou Sun; Neil Shah (ICLR, 2022)

From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness. - Lingxiao Zhao; Wei Jin 009; Leman Akoglu; Neil Shah (ICLR, 2022)

Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media? - Julie Jiang; Nils Murrugarra-Llerena; Maarten W. Bos; Yozen Liu; Neil Shah; Leonardo Neves; Francesco Barbieri (ICWSM, 2022)

Joint International Workshop on Misinformation and Misbehavior Mining on the Web & Making a Credible Web for Tomorrow (MIS2-TrueFact). - Pamela Bhattacharya; Jing Gao 004; Meng Jiang 001; Mehran Kafai; Srijan Kumar; Qi Li 012; Neil Shah; Sihong Xie; Philip S. Yu; Ming Zeng 001 (KDD, 2022)

17th International Workshop on Mining and Learning with Graphs (MLG). - Shobeir Fakhraei; Tim Weninger; Neil Shah; Sami Abu-El-Haija; Saurabh Verma; Tara Safavi (KDD, 2022)

Flashlight: Scalable Link Prediction With Effective Decoders. - Yiwei Wang 001; Bryan Hooi; Yozen Liu; Tong Zhao 003; Zhichun Guo; Neil Shah (LoG, 2022)

GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games. - Shichang Zhang; Yozen Liu; Neil Shah; Yizhou Sun (NeurIPS, 2022)

A Practical, Progressively-Expressive GNN. - Lingxiao Zhao; Neil Shah; Leman Akoglu (NeurIPS, 2022)

Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs. - Hyeonjeong Shin; Taehyung Kwon; Neil Shah; Kijung Shin (WSDM, 2022)

Attributed Graph Modeling with Vertex Replacement Grammars. - Satyaki Sikdar; Neil Shah; Tim Weninger (WSDM, 2022)

Friend Story Ranking with Edge-Contextual Local Graph Convolutions. - Xianfeng Tang; Yozen Liu; Xinran He; Suhang Wang; Neil Shah (WSDM, 2022)

Explaining Graph-level Predictions with Communication Structure-Aware Cooperative Games. - Shichang Zhang; Neil Shah; Yozen Liu; Yizhou Sun (CoRR, 2022)

Are Graph Neural Networks Really Helpful for Knowledge Graph Completion? - Juan-Hui Li; Harry Shomer; Jiayuan Ding; Yiqi Wang 001; Yao Ma 001; Neil Shah; Jiliang Tang; Dawei Yin (CoRR, 2022)

Forget Unlearning: Towards True Data-Deletion in Machine Learning. - Rishav Chourasia; Neil Shah; Reza Shokri (CoRR, 2022)

A Practical, Progressively-Expressive GNN. - Lingxiao Zhao; Louis Härtel; Neil Shah; Leman Akoglu (CoRR, 2022)

Editorial: Computational Behavioral Modeling for Big User Data. - Meng Jiang 001; Chuxu Zhang; Xiangliang Zhang 001; Neil Shah (Frontiers Big Data, 2022)

A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning. - Tong Zhao 003; Tianwen Jiang; Neil Shah; Meng Jiang 001 (IEEE Trans. Neural Networks Learn. Syst., 2022)

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? - Juanhui Li; Harry Shomer; Jiayuan Ding; Yiqi Wang 001; Yao Ma 001; Neil Shah; Jiliang Tang; Dawei Yin (ACL (1), 2023)

Empowering Graph Representation Learning with Test-Time Graph Transformation. - Wei Jin 009; Tong Zhao 003; Jiayuan Ding; Yozen Liu; Jiliang Tang; Neil Shah (ICLR, 2023)

MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization. - Xiaotian Han; Tong Zhao 003; Yozen Liu; Xia Hu 001; Neil Shah (ICLR, 2023)

Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization. - Mingxuan Ju; Tong Zhao 003; Qianlong Wen; Wenhao Yu 002; Neil Shah; Yanfang Ye 001; Chuxu Zhang (ICLR, 2023)

Link Prediction with Non-Contrastive Learning. - William Shiao; Zhichun Guo; Tong Zhao 003; Evangelos E. Papalexakis; Yozen Liu; Neil Shah (ICLR, 2023)

Forget Unlearning: Towards True Data-Deletion in Machine Learning. - Rishav Chourasia; Neil Shah (ICML, 2023)

Linkless Link Prediction via Relational Distillation. - Zhichun Guo; William Shiao; Shichang Zhang; Yozen Liu; Nitesh V. Chawla; Neil Shah; Tong Zhao 003 (ICML, 2023)

Predicting Future Location Categories of Users in a Large Social Platform. - Raiyan Abdul Baten; Yozen Liu; Heinrich Peters; Francesco Barbieri; Neil Shah; Leonardo Neves; Maarten W. Bos (ICWSM, 2023)

Sketch-Based Anomaly Detection in Streaming Graphs. - Siddharth Bhatia 001; Mohit Wadhwa; Kenji Kawaguchi; Neil Shah; Philip S. Yu; Bryan Hooi (KDD, 2023)

19th International Workshop on Mining and Learning with Graphs (MLG). - Neil Shah; Shobeir Fakhraei; Da Zheng; Bahare Fatemi; Leman Akoglu (KDD, 2023)

CARL-G: Clustering-Accelerated Representation Learning on Graphs. - William Shiao; Uday Singh Saini; Yozen Liu; Tong Zhao 003; Neil Shah; Evangelos E. Papalexakis (KDD, 2023)

Large-Scale Graph Neural Networks: The Past and New Frontiers. - Rui Xue 006; Haoyu Han 001; Tong Zhao 003; Neil Shah; Jiliang Tang; Xiaorui Liu (KDD, 2023)

GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation. - Mingxuan Ju; Tong Zhao 003; Wenhao Yu 002; Neil Shah; Yanfang Ye 001 (NeurIPS, 2023)

Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking. - Juanhui Li; Harry Shomer; Haitao Mao; Shenglai Zeng; Yao Ma 001; Neil Shah; Jiliang Tang; Dawei Yin (NeurIPS, 2023)

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? - Haitao Mao; Zhikai Chen; Wei Jin 009; Haoyu Han 001; Yao Ma 001; Tong Zhao 003; Neil Shah; Jiliang Tang (NeurIPS, 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, 2023)

The 3rd International Workshop on Machine Learning on Graphs (MLoG). - Tyler Derr; Yao Ma 001; Benedek Rozemberczki; Neil Shah; Shirui Pan (WSDM, 2023)

Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference. - Yiwei Wang 001; Bryan Hooi; Yozen Liu; Neil Shah (WSDM, 2023)

Graph Transformers for Large Graphs. - Vijay Prakash Dwivedi; Yozen Liu; Anh Tuan Luu; Xavier Bresson; Neil Shah; Tong Zhao 003 (CoRR, 2023)

Graph Data Augmentation for Graph Machine Learning: A Survey. - Tong Zhao 003; Wei Jin 009; Yozen Liu; Yingheng Wang; Gang Liu 025; Stephan Günnemann; Neil Shah; Meng Jiang 001 (IEEE Data Eng. Bull., 2023)

Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster. - Agostina Calabrese; Leonardo Neves; Neil Shah; Maarten W. Bos; Björn Ross; Mirella Lapata; Francesco Barbieri (ACL (Short Papers), 2024)

Learning from Graphs Beyond Message Passing Neural Networks. - Tong Zhao 003; Neil Shah; Elham Ghazizadeh (Tiny Papers @ ICLR, 2024)

A Topological Perspective on Demystifying GNN-Based Link Prediction Performance. - Yu Wang 160; Tong Zhao 003; Yuying Zhao; Yunchao Liu 001; Xueqi Cheng; Neil Shah; Tyler Derr (ICLR, 2024)

Revisiting Link Prediction: a data perspective. - Haitao Mao; Juanhui Li; Harry Shomer; Bingheng Li; Wenqi Fan; Yao Ma 001; Tong Zhao 003; Neil Shah; Jiliang Tang (ICLR, 2024)

LLaGA: Large Language and Graph Assistant. - Runjin Chen; Tong Zhao 003; Ajay Kumar Jaiswal; Neil Shah; Zhangyang Wang (ICML, 2024)

Position: Graph Foundation Models Are Already Here. - Haitao Mao; Zhikai Chen; Wenzhuo Tang; Jianan Zhao 002; Yao Ma 001; Tong Zhao 003; Neil Shah; Mikhail Galkin 001; Jiliang Tang (ICML, 2024)

FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. - Junyuan Hong; Carl Yang 001; Zhuangdi Zhu; Zheng Xu 002; Nathalie Baracaldo; Neil Shah; Salman Avestimehr; Jiayu Zhou (KDD, 2024)

Towards Neural Scaling Laws on Graphs. - Jingzhe Liu; Haitao Mao; Zhikai Chen; Tong Zhao 003; Neil Shah; Jiliang Tang (LoG, 2024)

How Does Message Passing Improve Collaborative Filtering? - Mingxuan Ju; William Shiao; Zhichun Guo; Yanfang Ye 001; Yozen Liu; Neil Shah; Tong Zhao 003 (NeurIPS, 2024)

Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems. - Matthew Kolodner; Mingxuan Ju; Zihao Fan; Tong Zhao 003; Elham Ghazizadeh; Yan Wu; Neil Shah; Yozen Liu (RobustRecSys@RecSys, 2024)

Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query Expansion. - Pau Perng-Hwa Kung; Zihao Fan; Tong Zhao 003; Yozen Liu; Zhixin Lai; Jiahui Shi; Yan Wu; Jun Yu; Neil Shah; Ganesh Venkataraman (SIGIR, 2024)

Neural Scaling Laws on Graphs. - Jingzhe Liu; Haitao Mao; Zhikai Chen; Tong Zhao 003; Neil Shah; Jiliang Tang (CoRR, 2024)

Graph Foundation Models. - Haitao Mao; Zhikai Chen; Wenzhuo Tang; Jianan Zhao 002; Yao Ma 001; Tong Zhao 003; Neil Shah; Mikhail Galkin 001; Jiliang Tang (CoRR, 2024)

Node Duplication Improves Cold-start Link Prediction. - Zhichun Guo; Tong Zhao 003; Yozen Liu; Kaiwen Dong; William Shiao; Neil Shah; Nitesh V. Chawla (CoRR, 2024)

Improving Out-of-Vocabulary Handling in Recommendation Systems. - William Shiao; Mingxuan Ju; Zhichun Guo; Xin Chen 085; Evangelos E. Papalexakis; Tong Zhao 003; Neil Shah; Yozen Liu (CoRR, 2024)

Multimodal Graph Benchmark. - Jing Zhu 005; Yuhang Zhou; Shengyi Qian 001; Zhongmou He; Tong Zhao 003; Neil Shah; Danai Koutra (CoRR, 2024)

Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks. - Rui Xue 006; Tong Zhao 003; Neil Shah; Xiaorui Liu (CoRR, 2024)

Hybrid-DAOs: Enhancing Governance, Scalability, and Compliance in Decentralized Systems. - Neil Shah (CoRR, 2024)

HARec: Hyperbolic Graph-LLM Alignment for Exploration and Exploitation in Recommender Systems. - Qiyao Ma; Menglin Yang 001; Mingxuan Ju; Tong Zhao 003; Neil Shah; Rex Ying (CoRR, 2024)

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs. - Jingzhe Liu; Haitao Mao; Zhikai Chen; Wenqi Fan; Mingxuan Ju; Tong Zhao 003; Neil Shah; Jiliang Tang (CoRR, 2024)

Enhancing Item Tokenization for Generative Recommendation through Self-Improvement. - Runjin Chen; Mingxuan Ju; Ngoc Bui; Dimosthenis Antypas; Stanley Cai; Xiaopeng Wu; Leonardo Neves; Zhangyang Wang; Neil Shah; Tong Zhao 003 (CoRR, 2024)

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. - Xinyi Wu 003; Donald Loveland; Runjin Chen; Yozen Liu; Xin Chen 085; Leonardo Neves; Ali Jadbabaie; Clark Mingxuan Ju; Neil Shah; Tong Zhao 003 (CoRR, 2024)

Unpacking the exploration-exploitation tradeoff on Snapchat: The relationships between users' exploration-exploitation interests and server log data. - Diego Gómez-Zará; Yozen Liu; Leonardo Neves; Neil Shah; Maarten W. Bos (Comput. Hum. Behav., 2024)

Guest Editorial: Special Issue on Graph Learning. - Feng Xia 001; Renaud Lambiotte; Neil Shah; Hanghang Tong; Irwin King (IEEE Trans. Neural Networks Learn. Syst., 2024)

Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning. - Jing Zhu 005; Yuhang Zhou; Shengyi Qian 001; Zhongmou He; Tong Zhao 003; Neil Shah; Danai Koutra (CVPR, 2025)

Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat. - Clark Mingxuan Ju; Leonardo Neves; Bhuvesh Kumar; Liam Collins; Tong Zhao 003; Yuwei Qiu; Qing Dou; Yang Zhou; Sohail Nizam; Rengim Aykan Ozturk; Yvette Liu; Sen Yang; et al. (SIGIR, 2025)

Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. - Donald Loveland; Xinyi Wu 003; Tong Zhao 003; Danai Koutra; Neil Shah; Mingxuan Ju (WWW, 2025)

Improving Out-of-Vocabulary Hashing in Recommendation Systems. - William Shiao; Mingxuan Ju; Zhichun Guo; Xin Chen 085; Evangelos E. Papalexakis; Tong Zhao 003; Neil Shah; Yozen Liu (WWW (Companion Volume), 2025)

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. - Xinyi Wu 003; Donald Loveland; Runjin Chen; Yozen Liu; Xin Chen 085; Leonardo Neves; Ali Jadbabaie; Mingxuan Ju; Neil Shah; Tong Zhao 003 (WWW, 2025)

Node Duplication Improves Cold-start Link Prediction. - Zhichun Guo; Tong Zhao 003; Yozen Liu; Kaiwen Dong; William Shiao; Mingxuan Ju; Neil Shah; Nitesh V. Chawla (Trans. Mach. Learn. Res., 2025)

Retrieval-Augmented Generation with Graphs (GraphRAG). - Haoyu Han 001; Yu Wang 160; Harry Shomer; Kai Guo 003; Jiayuan Ding; Yongjia Lei; Mahantesh Halappanavar; Ryan A. Rossi; Subhabrata Mukherjee; Xianfeng Tang; Qi He 002; Zhigang Hua; et al. (CoRR, 2025)

GiGL: Large-Scale Graph Neural Networks at Snapchat. - Tong Zhao 003; Yozen Liu; Matthew Kolodner; Kyle Montemayor; Elham Ghazizadeh; Ankit Batra; Zihao Fan; Xiaobin Gao; Xuan Guo; Jiwen Ren; Serim Park; Peicheng Yu; et al. (CoRR, 2025)

Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics. - Jing Zhu 005; Mingxuan Ju; Yozen Liu; Danai Koutra; Neil Shah; Tong Zhao 003 (CoRR, 2025)

Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction. - Zongyue Qin; Shichang Zhang; Mingxuan Ju; Tong Zhao 003; Neil Shah; Yizhou Sun (CoRR, 2025)

On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective. - Donald Loveland; Mingxuan Ju; Tong Zhao 003; Neil Shah; Danai Koutra (CoRR, 2025)

Revisiting Self-attention for Cross-domain Sequential Recommendation. - Clark Mingxuan Ju; Leonardo Neves; Bhuvesh Kumar; Liam Collins; Tong Zhao 003; Yuwei Qiu; Qing Dou; Sohail Nizam; Sen Yang; Neil Shah (CoRR, 2025)

Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning. - Ngoc Bui; Menglin Yang 001; Runjin Chen; Leonardo Neves; Mingxuan Ju; Rex Ying; Neil Shah; Tong Zhao 003 (CoRR, 2025)

A Pre-training Framework for Relational Data with Information-theoretic Principles. - Quang Truong; Zhikai Chen; Mingxuan Ju; Tong Zhao 003; Neil Shah; Jiliang Tang (CoRR, 2025)

Generative Recommendation with Semantic IDs: A Practitioner's Handbook. - Clark Mingxuan Ju; Liam Collins; Leonardo Neves; Bhuvesh Kumar; Louis Yufeng Wang; Tong Zhao 003; Neil Shah (CoRR, 2025)