Search Results for author: Xianfeng Tang

Found 26 papers, 10 papers with code

Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond

1 code implementation15 Mar 2024 Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang

Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.

Explanation Generation Image Generation

Language Models As Semantic Indexers

1 code implementation11 Oct 2023 Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, Xianfeng Tang

Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.

Contrastive Learning Information Retrieval +2

Self-Explainable Graph Neural Networks for Link Prediction

no code implementations21 May 2023 Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang, Junjie Xu, Hui Liu, Suhang Wang

Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.

Link Prediction Node Classification

HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers

no code implementations19 Feb 2023 Chen Liang, Haoming Jiang, Zheng Li, Xianfeng Tang, Bin Yin, Tuo Zhao

Since the teacher model has a significantly larger capacity and stronger representation power than the student model, it is very difficult for the student to produce predictions that match the teacher's over a massive amount of open-domain training data.

Knowledge Distillation Model Compression +1

DiP-GNN: Discriminative Pre-Training of Graph Neural Networks

no code implementations15 Sep 2022 Simiao Zuo, Haoming Jiang, Qingyu Yin, Xianfeng Tang, Bing Yin, Tuo Zhao

Specifically, we train a generator to recover identities of the masked edges, and simultaneously, we train a discriminator to distinguish the generated edges from the original graph's edges.

Node Classification

Condensing Graphs via One-Step Gradient Matching

3 code implementations15 Jun 2022 Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin

However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.

Dataset Condensation

MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose

no code implementations25 May 2022 Chenyan Wu, Yandong Li, Xianfeng Tang, James Wang

Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i. e., skeleton and mesh structure).

3D Multi-Person Human Pose Estimation 3D Multi-Person Pose Estimation

Task-Agnostic Graph Explanations

1 code implementation16 Feb 2022 Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji

They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks.

Task-Agnostic Graph Neural Explanations

no code implementations29 Sep 2021 Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward W Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji

TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.

Jointly Attacking Graph Neural Network and its Explanations

no code implementations7 Aug 2021 Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, JianPing Wang, Charu Aggarwal

Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.

Semi-Supervised Graph-to-Graph Translation

no code implementations16 Mar 2021 Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, Suhang Wang

For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain.

Graph-To-Graph Translation Translation

A Effective Carrier Phase Recovery Method in Tigth Time-Packing Fast than Nyquist Optical Communication System

no code implementations24 Aug 2020 Peng Sun, Xiaoguang Zhang, Dongwei Pan, Lixia Xi, Wenbo Zhang, Xianfeng Tang

We propose a new scheme that combines polybinary transformaton and corrected-BPS to compensate noise for PDM-FTN-QPSK when its accelerated factor is 0. 5, which has 3. 3 dB OSNR gain when phase noise is 800 kHz.

Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks

no code implementations28 Jun 2020 Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang

Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.

Self-Supervised Learning

Node Injection Attacks on Graphs via Reinforcement Learning

no code implementations14 Sep 2019 Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar

Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.

Node Classification reinforcement-learning +1

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

1 code implementation20 Aug 2019 Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang

To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.

Graph Neural Network Node Classification +1

MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

no code implementations20 Aug 2019 Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar

Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)

Generative Adversarial Network Link Prediction +3

Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

no code implementations25 Feb 2019 Xianfeng Tang, Boqing Gong, Yanwei Yu, Huaxiu Yao, Yandong Li, Haiyong Xie, Xiaoyu Wang

In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems.

Graph Embedding

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

5 code implementations3 Mar 2018 Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li

Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i. e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical.

Traffic Prediction

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

1 code implementation23 Feb 2018 Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li

Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.

Image Classification Time Series Forecasting +1

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