58 citations · 80 across the 15 of their papers we have counts for
6 papers
Virtual Node Tuning for Few-shot Node Classification
Zhen Tan, Ruocheng Guo, Kaize Ding +1
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-…
AutoMLP: Automated MLP for Sequential Recommendations
Muyang Li, Zijian Zhang, Xiangyu Zhao +4
Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' l…
Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang, Xiaoying Zhang, Yang Liu +4
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic st…
Graph Few-shot Class-incremental Learning
Zhen Tan, Kaize Ding, Ruocheng Guo +1
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommenda…
Toward Early and Order-of-Magnitude Cascade Prediction in Social Networks
Ruocheng Guo, Elham Shaabani, Abhinav Bhatnagar +1
When a piece of information (microblog, photograph, video, link, etc.) starts to spread in a social network, an important question arises: will it spread to viral proportions - whe…
An Empirical Evaluation Of Social Influence Metrics
Nikhil Kumar, Ruocheng Guo, Ashkan Aleali +1
Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the perfor- mance of…