364 citations · 460 across the 28 of their papers we have counts for
13 papers · 1 filter
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang +4
Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing…
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
Chunjing Gan, Binbin Hu, Bo Huang +6
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspe…
InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs
Dalong Zhang, Xianzheng Song, Zhiyang Hu +6
GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge…
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
Weifan Wang, Binbin Hu, Zhicheng Peng +5
Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience…
Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce
Borui Ye, Shuo Yang, Binbin Hu +5
E-commerce has gone a long way in empowering merchants through the internet. In order to store the goods efficiently and arrange the marketing resource properly, it is important fo…
KGNN: Distributed Framework for Graph Neural Knowledge Representation
Binbin Hu, Zhiyang Hu, Zhiqiang Zhang +2
Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services. Although existing knowledge representation learning me…
Neural Graph Matching for Pre-training Graph Neural Networks
Yupeng Hou, Binbin Hu, Wayne Xin Zhao +3
Recently, graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. However, when adapted to downstream tasks, it usually requires abundant task-s…
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
Hongrui Liu, Binbin Hu, Xiao Wang +3
Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful trai…
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao, Lingxiao Wang, Ziqi Liu +4
Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling…
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Zhibo Zhu, Ziqi Liu, Ge Jin +4
Time series forecasting is widely used in business intelligence, e.g., forecast stock market price, sales, and help the analysis of data trend. Most time series of interest are mac…
Bandit Samplers for Training Graph Neural Networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang +4
Several sampling algorithms with variance reduction have been proposed for accelerating the training of Graph Convolution Networks (GCNs). However, due to the intractable computati…
NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay
Jianbin Lin, Zhiqiang Zhang, Jun Zhou +5
Ant Credit Pay is a consumer credit service in Ant Financial Service Group. Similar to credit card, loan default is one of the major risks of this credit product. Hence, effective…