Junjie Huang

(黄俊杰)

Introduction

Hi! 👋

In 2023, I joined the Computer and Information Science department at Southwest University (SWU) after obtaining my Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS).

In short, 🎓 PKUer -> ICTer -> SWUer.

I was advised by Prof. Xueqi Cheng and Prof. Huawei Shen.

My research focuses on computational social sciences fields (CSS). Computational social science is a broad field that necessitates interdisciplinary and highly collaborative researchers. You are welcome to email me if you have any promising and challenging CSS research problems.

Currently, I am working on modeling signed social networks (i.e., social networks with both positive and negative links) using graph representation learning.

More recently, I have also been interested in social agents, especially LLM-driven agents for social simulation, human-agent interaction, and broader social computing applications (e.g., Education).

Education && Experience

• Associate Professor, College of Computer and Information Science, Chongqing, Southwest University 2023-
  Research Interest: Computational Social Science (Social Computing), Social Network Analysis.
  Department: Computer Science
• Ph.D., Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. 2017 - 2023
  Advisor: Prof. Xueqi Cheng and Prof. Huawei Shen
  Ph.D. Topic: Signed Graph Representation Learning
• B.S., Department of Information Management, Peking University, Beijing, China. 2013 - 2017
  Bachelor of Science in Information Management & Information System Bachelor of Science in Computer Science & Technology (Double Major)

Selected Publications

Full publications see Google Scholar

1. Inductive Dual-Polarity Modeling via Static–Dynamic Disentanglement for Dynamic Signed Networks
   Yikang Hou, Junjie Huang★, Yijun Ran, Tao Jia

   C 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2026) , Full Research Paper

   [Paper] [Summary]

   

   TL;DR: We propose a new framework for inductive signed dynamic edge prediction, and experiments on four benchmarks show consistent improvements over existing baselines.

2. LIR$^3$AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation
   Guo Chen, Junjie Huang★, Huaijin Xie, Fei Sun, Tao Jia

   C 40th AAAI Conference on Artificial Intelligence (AAAI2026) , Full Research Paper

   [Paper][Slides][Poster][Github] [Summary]

   

   TL;DR: LiR$^3$AG (Lightweight Rerank Reasoning for RAG) is a framework designed to enhance non-reasoning LLMs by structuring retrieved evidence into coherent, logical reasoning chains. It optimizes RAG by identifying, reranking, and constructing concise, relevant, and accurate answers, significantly reducing inference time (58.6%) and token overhead (98%) while boosting performance in multi-hop question-answering tasks.

3. What makes a successful rebuttal in computer science conferences? : A perspective on social interaction
   Junjie Huang, Win-bin Huang, Yi Bu, Qi Cao, Huawei Shen, Xueqi Cheng

   J Journal of Informetrics (JOI) , Volume 17, Issue 3, August 2023, 101427

   [Paper][Poster] [Summary]

   

   TL;DR: Illustration of peer review process in ICLR2022. ICLR2022 mainly includes four stages: initial review, author response, reviewer discussion and final decision. In this paper, we mainly focus on the rebuttal stage between t1 and t2.

4. Negative Can Be Positive: Signed Graph Neural Networks for Recommendation
   Junjie Huang, Ruobing Xie, Qi Cao, Huawei Shen, Shaoliang Zhang, Feng Xia, Xueqi Cheng

   J Information Processing & Management (IPM) , Volume 60, Issue 4, July 2023, 103403

   [Paper][Poster] [Summary]

   

   TL;DR: In this paper, we study the negative feedback in the recommender system, which is of great importance. We qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems. Our method is parameter-efficient for handling scenarios where negative feedback data is insufficient. Our methods outperform existing models on several real-world datasets.

5. Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation
   Junjie Huang, Qi Cao, Ruobing Xie, Shaoliang Zhang, Feng Xia, Huawei Shen, Xueqi Cheng

   C 28th International Conference on Database Systems for Advanced Applications (DASFAA2023) , Regular Paper

   [Paper][Slides][Github] [Summary]

   

   TL;DR: In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable Data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively.

6. Signed Bipartite Graph Neural Networks
   Junjie Huang, Huawei Shen, Qi Cao, Shuchang Tao, Xueqi Cheng

   C 30th ACM International Conference on Information and Knowledge Management (CIKM2021) , Full Research Paper

   [Paper][Slides][Poster][Github] [Summary]

   

   TL;DR: Signed bipartite networks can be commonly found in many fields including business, politics, and academics, but have been less studied. We do some comprehensive analysis of balance theory from two perspectives on several real-world datasets. We propose a novel Signed Bipartite Graph Neural Networks (SBGNNs) to learn node embeddings for signed bipartite networks.

7. How Medical Crowdfunding Helps People? A Large-scale Case Study on Waterdrop Fundraising
   Junjie Huang, Huawei Shen, Qi Cao, Li Cai, Xueqi Cheng

   C 15th International AAAI Conference on Web and Social Media (ICWSM2021) , Full Paper

   [Paper][Slides] [Summary]

   

   TL;DR: The spread of fundraising information along social network, is a key factor of fundraising success, and the social capital of fundraisers play an important role in medical fundraising.

8. SDGNN: Learning Node Representation for Signed Directed Networks
   Junjie Huang, Huawei Shen, Liang Hou, Xueqi Cheng

   C 35th AAAI Conference on Artificial Intelligence (AAAI2021) , Full Paper

   [Paper][Github] [Summary]

   

   TL;DR: We propose a novel Signed Directed Graph Neural Networks model (i.e., SDGNN) to learn node embeddings for signed directed networks. Guided by two theories, our SBGNN model not only uses Signed Directed Aggregators but also simultaneously reconstructs link signs, link directions, and signed directed triangles.

Selected Awards

Academic Services

• Conference Reviewers

SIGIR, AAAI, WWW, KDD, CIKM, WSDM

• Journal Reviewers

IEEE Transactions on Knowledge and Data Engineering, Journal of Neural Networks, IEEE Transactions on Computational Social Systems, Information Processing and Management, Plos One, Expert Systems with Applications,