144 citations · 391 across the 88 of their papers we have counts for
10 papers · 1 filter
NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework
Shicheng Xu, Liang Pang, Huawei Shen +1
Information retrieval aims to find information that meets users' needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain que…
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
Yige Yuan, Bingbing Xu, Huawei Shen +4
Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (Info…
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…
Adversarial for Social Privacy: A Poisoning Strategy to Degrade User Identity Linkage
Jiangli Shao, Yongqing Wang, Boshen Shi +3
Privacy issues on social networks have been extensively discussed in recent years. The user identity linkage (UIL) task, aiming at finding corresponding users across different soci…
Adversarial Camouflage for Node Injection Attack on Graphs
Shuchang Tao, Qi Cao, Huawei Shen +4
Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates.…
Learning node embeddings via summary graphs: a brief theoretical analysis
Houquan Zhou, Shenghua Liu, Danai Koutra +2
Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem. Recent works try to improv…
Few-Shot Stance Detection via Target-Aware Prompt Distillation
Yan Jiang, Jinhua Gao, Huawei Shen +1
Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learn…
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
Liang Hou, Qi Cao, Yige Yuan +7
Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation…
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors
Quan Ding, Shenghua Liu, Bin Zhou +2
Given a multivariate big time series, can we detect anomalies as soon as they occur? Many existing works detect anomalies by learning how much a time series deviates away from what…
Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification
Zhaohui Wang, Qi Cao, Huawei Shen +2
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we propose a novel graph isomorphism t…