78 citations · 277 across the 32 of their papers we have counts for
8 papers · 1 filter
MIDSim: Simulating Multi-Channel Information Diffusion in Social Media with LLM-Powered Multi-Agent System
Lexi Liu, Qi Cao, Yuanhao Liu +2
Information diffusion in social media shapes public opinion and collective behavior, making its modeling and simulation an important research problem. Existing studies have investi…
What makes a successful rebuttal in computer science conferences? : A perspective on social interaction
Junjie Huang, Win-bin Huang, Yi Bu +3
With an exponential increase in submissions to top-tier Computer Science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review…
DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph
Kaike Zhang, Qi Cao, Gaolin Fang +4
Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graph, the dynamic graph is a comprehensive…
Signed Bipartite Graph Neural Networks
Junjie Huang, Huawei Shen, Qi Cao +2
Signed networks are such social networks having both positive and negative links. A lot of theories and algorithms have been developed to model such networks (e.g., balance theory)…
PREP: Pre-training with Temporal Elapse Inference for Popularity Prediction
Qi Cao, Huawei Shen, Yuanhao Liu +2
Predicting the popularity of online content is a fundamental problem in various applications. One practical challenge takes roots in the varying length of observation time or predi…
How Medical Crowdfunding Helps People? A Large-scale Case Study on Waterdrop Fundraising
Junjie Huang, Huawei Shen, Qi Cao +2
While online medical crowdfunding achieved tremendous success, quantitative study about whether and how medical crowdfunding helps people remains little explored. In this paper, we…
Popularity Prediction on Social Platforms with Coupled Graph Neural Networks
Qi Cao, Huawei Shen, Jinhua Gao +2
Predicting the popularity of online content on social platforms is an important task for both researchers and practitioners. Previous methods mainly leverage demographics, temporal…
ANAE: Learning Node Context Representation for Attributed Network Embedding
Keting Cen, Huawei Shen, Jinhua Gao +3
Attributed network embedding aims to learn low-dimensional node representations from both network structure and node attributes. Existing methods can be categorized into two groups…