Toward Order-of-Magnitude Cascade Prediction. - Ruocheng Guo; Elham Shaabani; Abhinav Bhatnagar; Paulo Shakarian (ASONAM, 2015)

Diffusion in Social Networks - Paulo Shakarian; Abhinav Bhatnagar; Ashkan Aleali; Elham Shaabani; Ruocheng Guo (Springer, 2015)

A comparison of methods for cascade prediction. - Ruocheng Guo; Paulo Shakarian (ASONAM, 2016)

An empirical evaluation of social influence metrics. - Nikhil Kumar 006; Ruocheng Guo; Ashkan Aleali; Paulo Shakarian (ASONAM, 2016)

Toward early and order-of-magnitude cascade prediction in social networks. - Ruocheng Guo; Elham Shaabani; Abhinav Bhatnagar; Paulo Shakarian (Soc. Netw. Anal. Min., 2016)

Temporal Analysis of Influence to Predict Users' Adoption in Online Social Networks. - Ericsson Marin; Ruocheng Guo; Paulo Shakarian (SBP-BRiMS, 2017)

Understanding and forecasting lifecycle events in information cascades. - Soumajyoti Sarkar; Ruocheng Guo; Paulo Shakarian (Soc. Netw. Anal. Min., 2017)

Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects. - Vineeth Rakesh; Ruocheng Guo; Raha Moraffah; Nitin Agarwal 001; Huan Liu 001 (CIKM, 2018)

Detecting Pathogenic Social Media Accounts without Content or Network Structure. - Elham Shaabani; Ruocheng Guo; Paulo Shakarian (ICDIS, 2018)

INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process. - Ruocheng Guo; Jundong Li; Huan Liu 001 (IJCAI, 2018)

Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression. - Ruocheng Guo; Hamidreza Alvari; Paulo Shakarian (SDM, 2018)

Multi-level network embedding with boosted low-rank matrix approximation. - Jundong Li; Liang Wu 006; Ruocheng Guo; Chenghao Liu; Huan Liu 001 (ASONAM, 2019)

A Practical Data Repository for Causal Learning with Big Data. - Lu Cheng 001; Ruocheng Guo; Raha Moraffah; K. Selçuk Candan; Adrienne Raglin; Huan Liu 001 (Bench, 2019)

Causal Learning in Question Quality Improvement. - Yichuan Li 001; Ruocheng Guo; Weiying Wang; Huan Liu 001 (Bench, 2019)

Privacy Preserving Text Representation Learning. - Ghazaleh Beigi; Kai Shu; Ruocheng Guo; Suhang Wang; Huan Liu 001 (HT, 2019)

Adaptive Unsupervised Feature Selection on Attributed Networks. - Jundong Li; Ruocheng Guo; Chenghao Liu; Huan Liu 001 (KDD, 2019)

Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network. - Lu Cheng 001; Ruocheng Guo; Yasin N. Silva; Deborah L. Hall; Huan Liu 001 (SDM, 2019)

Protecting User Privacy: An Approach for Untraceable Web Browsing History and Unambiguous User Profiles. - Ghazaleh Beigi; Ruocheng Guo; Alexander Nou; Yanchao Zhang; Huan Liu 001 (WSDM, 2019)

Robust Cyberbullying Detection with Causal Interpretation. - Lu Cheng 001; Ruocheng Guo; Huan Liu 001 (WWW (Companion Volume), 2019)

Learning Individual Treatment Effects from Networked Observational Data. - Ruocheng Guo; Jundong Li; Huan Liu 001 (CoRR, 2019)

I Am Not What I Write: Privacy Preserving Text Representation Learning. - Ghazaleh Beigi; Kai Shu; Ruocheng Guo; Suhang Wang; Huan Liu 001 (CoRR, 2019)

Using network motifs to characterize temporal network evolution leading to diffusion inhibition. - Soumajyoti Sarkar; Ruocheng Guo; Paulo Shakarian (Soc. Netw. Anal. Min., 2019)

IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data. - Ruocheng Guo; Jundong Li; Yichuan Li 001; K. Selçuk Candan; Adrienne Raglin; Huan Liu 001 (IJCAI, 2020)

Debiasing Grid-based Product Search in E-commerce. - Ruocheng Guo; Xiaoting Zhao; Adam Henderson; Liangjie Hong; Huan Liu 001 (KDD, 2020)

Representation Learning for Imbalanced Cross-Domain Classification. - Lu Cheng 001; Ruocheng Guo; K. Selçuk Candan; Huan Liu 001 (SDM, 2020)

Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data. - Ruocheng Guo; Jundong Li; Huan Liu 001 (SDM, 2020)

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning. - Ghazaleh Beigi; Ahmadreza Mosallanezhad; Ruocheng Guo; Hamidreza Alvari; Alexander Nou; Huan Liu 001 (WSDM, 2020)

Learning Individual Causal Effects from Networked Observational Data. - Ruocheng Guo; Jundong Li; Huan Liu 001 (WSDM, 2020)

Towards Causal Understanding of Fake News Dissemination. - Lu Cheng 001; Ruocheng Guo; Kai Shu; Huan Liu 001 (CoRR, 2020)

Causal Interpretability for Machine Learning - Problems, Methods and Evaluation. - Raha Moraffah; Mansooreh Karami; Ruocheng Guo; Adrienne Raglin; Huan Liu 001 (SIGKDD Explor., 2020)

Measuring Time-Constrained Influence to Predict Adoption in Online Social Networks. - Ericsson Marin; Ruocheng Guo; Paulo Shakarian (ACM Trans. Soc. Comput., 2020)

CauseBox: A Causal Inference Toolbox for BenchmarkingTreatment Effect Estimators with Machine Learning Methods. - Paras Sheth; Ujun Jeong; Ruocheng Guo; Huan Liu 001; K. Selçuk Candan (CIKM, 2021)

Multi-Cause Effect Estimation with Disentangled Confounder Representation. - Jing Ma 002; Ruocheng Guo; Aidong Zhang; Jundong Li (IJCAI, 2021)

Causal Understanding of Fake News Dissemination on Social Media. - Lu Cheng 001; Ruocheng Guo; Kai Shu; Huan Liu 001 (KDD, 2021)

Long-Term Effect Estimation with Surrogate Representation. - Lu Cheng 001; Ruocheng Guo; Huan Liu 001 (WSDM, 2021)

Deconfounding with Networked Observational Data in a Dynamic Environment. - Jing Ma 002; Ruocheng Guo; Chen Chen 022; Aidong Zhang; Jundong Li (WSDM, 2021)

Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix. - Ruocheng Guo; Pengchuan Zhang; Hao Liu; Emre Kiciman (CoRR, 2021)

A Survey of Learning Causality with Data: Problems and Methods. - Ruocheng Guo; Lu Cheng 001; Jundong Li; P. Richard Hahn; Huan Liu 001 (ACM Comput. Surv., 2021)

Adversarial Attacks and Defenses: An Interpretation Perspective. - Ninghao Liu; Mengnan Du; Ruocheng Guo; Huan Liu 001; Xia Hu 001 (SIGKDD Explor., 2021)

Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks. - Lu Cheng 001; Ruocheng Guo; Yasin N. Silva; Deborah L. Hall; Huan Liu 001 (Trans. Data Sci., 2021)

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks. - Ujun Jeong; Kaize Ding; Lu Cheng 001; Ruocheng Guo; Kai Shu; Huan Liu 001 (IEEE Big Data, 2022)

Distributional Shift Adaptation using Domain-Specific Features. - Anique Tahir; Lu Cheng 001; Ruocheng Guo; Huan Liu 001 (IEEE Big Data, 2022)

Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective. - Weijieying Ren; Lei Wang 185; Kunpeng Liu 001; Ruocheng Guo; Ee-Peng Lim; Yanjie Fu (ICDM, 2022)

Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication. - Lu Cheng 001; Ruocheng Guo; Kasim Selçuk Candan; Huan Liu 001 (ICWSM, 2022)

MLP4Rec: A Pure MLP Architecture for Sequential Recommendations. - Muyang Li; Xiangyu Zhao 001; Chuan Lyu; Minghao Zhao 002; Runze Wu 001; Ruocheng Guo (IJCAI, 2022)

CLEAR: Generative Counterfactual Explanations on Graphs. - Jing Ma 002; Ruocheng Guo; Saumitra Mishra; Aidong Zhang; Jundong Li (NeurIPS, 2022)

Supervised Graph Contrastive Learning for Few-Shot Node Classification. - Zhen Tan 001; Kaize Ding; Ruocheng Guo; Huan Liu 001 (ECML/PKDD (2), 2022)

Causal Disentanglement with Network Information for Debiased Recommendations. - Paras Sheth; Ruocheng Guo; Kaize Ding; Lu Cheng 001; K. Selçuk Candan; Huan Liu 001 (SISAP, 2022)

Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies. - Lu Cheng 001; Ruocheng Guo; Huan Liu 001 (WSDM, 2022)

Causal Mediation Analysis with Hidden Confounders. - Lu Cheng 001; Ruocheng Guo; Huan Liu 001 (WSDM, 2022)

Learning Fair Node Representations with Graph Counterfactual Fairness. - Jing Ma 002; Ruocheng Guo; Mengting Wan; Longqi Yang; Aidong Zhang; Jundong Li (WSDM, 2022)

Graph Few-shot Class-incremental Learning. - Zhen Tan 001; Kaize Ding; Ruocheng Guo; Huan Liu 001 (WSDM, 2022)

Accurate identification of bacteriophages from metagenomic data using Transformer. - Jiayu Shang; Xubo Tang; Ruocheng Guo; Yanni Sun (Briefings Bioinform., 2022)

A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning. - Zhen Tan 001; Kaize Ding; Ruocheng Guo; Huan Liu 001 (CoRR, 2022)

Causal Disentanglement with Network Information for Debiased Recommendations. - Paras Sheth; Ruocheng Guo; Lu Cheng 001; Huan Liu 001; K. Selçuk Candan (CoRR, 2022)

Evaluation Methods and Measures for Causal Learning Algorithms. - Lu Cheng 001; Ruocheng Guo; Raha Moraffah; Paras Sheth; K. Selçuk Candan; Huan Liu 001 (IEEE Trans. Artif. Intell., 2022)

Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance. - Song Wang 013; Zhen Tan 001; Ruocheng Guo; Jundong Li (EMNLP (Findings), 2023)

Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization. - Maolin Wang 001; Dun Zeng; Zenglin Xu; Ruocheng Guo; Xiangyu Zhao 001 (ICDM, 2023)

Learning for Counterfactual Fairness from Observational Data. - Jing Ma 002; Ruocheng Guo; Aidong Zhang; Jundong Li (KDD, 2023)

Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems. - Xiaohui Chen; Jiankai Sun; Taiqing Wang; Ruocheng Guo; Li-Ping Liu 001; Aonan Zhang (KDD, 2023)

Virtual Node Tuning for Few-shot Node Classification. - Zhen Tan 001; Ruocheng Guo; Kaize Ding; Huan Liu 001 (KDD, 2023)

Debiasing Recommendation by Learning Identifiable Latent Confounders. - Qing Zhang; Xiaoying Zhang; Yang Liu 018; Hongning Wang; Min Gao 001; Jiheng Zhang; Ruocheng Guo (KDD, 2023)

Equal Opportunity of Coverage in Fair Regression. - Fangxin Wang 003; Lu Cheng 001; Ruocheng Guo; Kay Liu; Philip S. Yu (NeurIPS, 2023)

What Boosts Fake News Dissemination on Social Media? A Causal Inference View. - Yichuan Li 001; Kyumin Lee; Nima Kordzadeh; Ruocheng Guo (PAKDD (4), 2023)

AutoMLP: Automated MLP for Sequential Recommendations. - Muyang Li; Zijian Zhang 009; Xiangyu Zhao 001; Wanyu Wang; Minghao Zhao 002; Runze Wu 001; Ruocheng Guo (WWW, 2023)

Tensorized Hypergraph Neural Networks. - Maolin Wang 001; Yaoming Zhen; Yu Pan 005; Zenglin Xu; Ruocheng Guo; Xiangyu Zhao 001 (CoRR, 2023)

Fair Learning to Rank with Distribution-free Risk Control. - Ruocheng Guo; Jean-François Ton; Yang Liu 018 (CoRR, 2023)

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment. - Yang Liu 018; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq; Hang Li 001 (CoRR, 2023)

Deep Concept Removal. - Yegor Klochkov; Jean-Francois Ton; Ruocheng Guo; Yang Liu 018; Hang Li 001 (CoRR, 2023)

Embedding in Recommender Systems: A Survey. - Xiangyu Zhao 001; Maolin Wang 001; Xinjian Zhao; Jiansheng Li; Shucheng Zhou; Dawei Yin; Qing Li 001; Jiliang Tang; Ruocheng Guo (CoRR, 2023)

Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup. - Maolin Wang 001; Yao Zhao; Jiajia Liu 002; Jingdong Chen; Chenyi Zhuang; Jinjie Gu; Ruocheng Guo; Xiangyu Zhao 001 (CoRR, 2023)

Causal Disentanglement for Implicit Recommendations with Network Information. - Paras Sheth; Ruocheng Guo; Lu Cheng 001; Huan Liu 001; Kasim Selçuk Candan (ACM Trans. Knowl. Discov. Data, 2023)

Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media. - Bohan Jiang; Lu Cheng 001; Zhen Tan 001; Ruocheng Guo; Huan Liu 001 (DSAA, 2024)

Fair Classifiers that Abstain without Harm. - Tongxin Yin; Jean-Francois Ton; Ruocheng Guo; Yuanshun Yao; Mingyan Liu; Yang Liu 018 (ICLR, 2024)

Conformal Counterfactual Inference under Hidden Confounding. - Zonghao Chen; Ruocheng Guo; Jean-Francois Ton; Yang Liu 018 (KDD, 2024)

Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards. - Qinwei Yang; Xueqing Liu; Yan Zeng 002; Ruocheng Guo; Yang Liu 018; Peng Wu 012 (NeurIPS, 2024)

DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems. - Sheng Zhang 028; Maolin Wang 001; Xiangyu Zhao 001; Ruocheng Guo; Yao Zhao; Chenyi Zhuang; Jinjie Gu; Zijian Zhang 009; Hongzhi Yin (RecSys, 2024)

Tensorized Hypergraph Neural Networks. - Maolin Wang 001; Yaoming Zhen; Yu Pan 005; Yao Zhao; Chenyi Zhuang; Zenglin Xu; Ruocheng Guo; Xiangyu Zhao 001 (SDM, 2024)

Large Multimodal Model Compression via Iterative Efficient Pruning and Distillation. - Maolin Wang 001; Yao Zhao; Jiajia Liu 002; Jingdong Chen; Chenyi Zhuang; Jinjie Gu; Ruocheng Guo; Xiangyu Zhao 001 (WWW (Companion Volume), 2024)

Cumulative Distribution Function based General Temporal Point Processes. - Maolin Wang 001; Yu Pan 005; Zenglin Xu; Ruocheng Guo; Xiangyu Zhao 001; Wanyu Wang; Yiqi Wang 001; Zitao Liu 001; Langming Liu (CoRR, 2024)

EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems. - Sheng Zhang 028; Maolin Wang 001; Yao Zhao; Chenyi Zhuang; Jinjie Gu; Ruocheng Guo; Xiangyu Zhao 001; Zijian Zhang 009; Hongzhi Yin (CoRR, 2024)

Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction. - Chen Wang 018; Fangxin Wang 003; Ruocheng Guo; Yueqing Liang; Kay Liu; Philip S. Yu (CoRR, 2024)

Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation. - Xinjian Zhao; Liang Zhang; Yang Liu 018; Ruocheng Guo; Xiangyu Zhao 001 (CoRR, 2024)

Large Language Models for Data Annotation: A Survey. - Zhen Tan 001; Alimohammad Beigi; Song Wang 013; Ruocheng Guo; Amrita Bhattacharjee; Bohan Jiang; Mansooreh Karami; Jundong Li; Lu Cheng 001; Huan Liu 001 (CoRR, 2024)

Stepwise Reasoning Error Disruption Attack of LLMs. - Jingyu Peng; Maolin Wang 001; Xiangyu Zhao 001; Kai Zhang 038; Wanyu Wang; Pengyue Jia; Qidong Liu 002; Ruocheng Guo; Qi Liu 003 (CoRR, 2024)

Modeling Interference for Individual Treatment Effect Estimation from Networked Observational Data. - Qiang Huang; Jing Ma 002; Jundong Li; Ruocheng Guo; Huiyan Sun; Yi Chang 001 (ACM Trans. Knowl. Discov. Data, 2024)

SMLP4Rec: An Efficient All-MLP Architecture for Sequential Recommendations. - Jingtong Gao; Xiangyu Zhao 001; Muyang Li; Minghao Zhao 002; Runze Wu 001; Ruocheng Guo; Yiding Liu; Dawei Yin (ACM Trans. Inf. Syst., 2024)

Stepwise Reasoning Disruption Attack of LLMs. - Jingyu Peng; Maolin Wang 001; Xiangyu Zhao 001; Kai Zhang 038; Wanyu Wang; Pengyue Jia; Qidong Liu 002; Ruocheng Guo; Qi Liu 003 (ACL (1), 2025)

MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-Tuning. - Maolin Wang 001; Xiangyu Zhao 001; Ruocheng Guo; Junhui Wang (ICDE, 2025)

STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation. - Maolin Wang 001; Sheng Zhang 028; Ruocheng Guo; Wanyu Wang; Xuetao Wei; Zitao Liu 001; Hongzhi Yin; Yi Chang 001; Xiangyu Zhao 001 (SIGIR, 2025)

Optimal Policy Adaptation under Covariate Shift. - Xueqing Liu; Qinwei Yang; Zhaoqing Tian; Ruocheng Guo; Peng Wu 012 (CoRR, 2025)

Learning Counterfactual Outcomes Under Rank Preservation. - Peng Wu 012; Haoxuan Li 001; Chunyuan Zheng 001; Yan Zeng 002; Jiawei Chen; Yang Liu 018; Ruocheng Guo; Kun Zhang 001 (CoRR, 2025)

Identification and Estimation of Long-Term Treatment Effects with Monotone Missing. - Qinwei Yang; Ruocheng Guo; Shasha Han; Peng Wu 012 (CoRR, 2025)

FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation. - Maolin Wang 001; Yutian Xiao; Binhao Wang; Sheng Zhang 028; Shanshan Ye; Wanyu Wang; Hongzhi Yin; Ruocheng Guo; Zenglin Xu (CoRR, 2025)

DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation. - Maolin Wang 001; Tianshuo Wei; Sheng Zhang 028; Ruocheng Guo; Wanyu Wang; Shanshan Ye; Lixin Zou; Xuetao Wei; Xiangyu Zhao 001 (CoRR, 2025)