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Xianzhi Wang

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9 papers

1 author row

EAAI Journal 2024 Journal Article

Exploring explicit and implicit graph learning for multivariate time series imputation

Yakun Chen
Ruotong Hu
Zihao Li
Chao Yang
Xianzhi Wang
Guodong Long
Guandong Xu

Multivariate time series inherently contain missing values due to various issues, including incorrect data entry, broken equipment, and package loss during data transferring. The successful completion of time series data analysis tasks heavily relies on the essential task of imputing missing values. Inter-variable relationships in time series are typically overlooked by missing value imputation techniques. Although some graph-based algorithms can capture these relationships, the design of graph structures is commonly handcrafted and dataset-centric. We introduce a novel Explicit and Implicit Graph Recurrent Network (EIGRN) for multivariate time series imputation that integrates graph and recurrent neural networks to capture variable and time dependencies together. This proficiency is achieved by effectively integrating external data sources such as domain knowledge and the implicit relationships among nodes. In order to make our approach more applicable to datasets with larger numbers of missing values, we additionally discuss the model’s performance for various missing value ratios. Our comprehensive experiments on real-world datasets show that our model outperforms state-of-the-art baselines in different industrial fields.

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IJCAI Conference 2024 Conference Paper

KG-CoT: Chain-of-Thought Prompting of Large Language Models over Knowledge Graphs for Knowledge-Aware Question Answering

Ruilin Zhao
Feng Zhao
Long Wang
Xianzhi Wang
Guandong Xu

Large language models (LLMs) encounter challenges such as hallucination and factual errors in knowledge-intensive tasks. One the one hand, LLMs sometimes struggle to generate reliable answers based on the black-box parametric knowledge, due to the lack of responsible knowledge. Moreover, fragmented knowledge facts extracted by knowledge retrievers fail to provide explicit and coherent reasoning paths for improving LLM reasoning. To address these challenges, we propose KG-CoT, a novel knowledge-augmented paradigm that leverages a small-scale step-by-step graph reasoning model to reason over knowledge graphs (KGs) and utilizes a reasoning path generation method to generate chains of reasoning with high confidence for large-scale LLMs. Extensive experiments demonstrate that our KG-CoT significantly improves the performance of LLMs on knowledge-intensive question answering tasks, such as multi-hop, single-hop, and open-domain question answering benchmarks, without fine-tuning LLMs. KG-CoT outperforms the CoT prompting as well as prior retrieval-augmented and knowledge base question answering baselines. Moreover, KG-CoT can reduce the number of API calls and cost and generalize to various LLM backbones in a lightweight plug-and-play manner.

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AAAI Conference 2021 Conference Paper

Task Aligned Generative Meta-learning for Zero-shot Learning

Zhe Liu
Yun Li
Lina Yao
Xianzhi Wang
Guodong Long

Zero-shot learning (ZSL) refers to the problem of learning to classify instances from novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer the correlation between visual features and attributes to train the classifier for unseen classes. They may have a strong bias towards seen classes during training. Meta-learning has been introduced to mitigate the basis, but meta-ZSL methods are inapplicable when tasks used for training are sampled from diverse distributions. In this regard, we propose a novel Task-aligned Generative Meta-learning model for Zeroshot learning (TGMZ), aiming to mitigate the potentially biased training and to enable meta-ZSL to accommodate realworld datasets that contain diverse distributions. Specifically, TGMZ incorporates an attribute-conditioned task-wise distribution alignment network that projects tasks into a unified distribution to deliver an unbiased model. Our experiments show TGMZ achieves a relative improvement of 2. 1%, 3. 0%, 2. 5%, and 7. 6% over state-of-the-art algorithms on AWA1, AWA2, CUB, and aPY datasets, respectively. Overall, TGMZ outperforms competitors by 3. 6% in the generalized zero-shot learning (GZSL) setting and 7. 9% in our proposed fusion- ZSL setting.

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NeurIPS Conference 2020 Conference Paper

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

Lei Bai
Lina Yao
Can Li
Xianzhi Wang
Can Wang

Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system. Recent works focus on designing complicated graph neural network architectures to capture shared patterns with the help of pre-defined graphs. In this paper, we argue that learning node-specific patterns is essential for traffic forecasting while pre-defined graph is avoidable. To this end, we propose two adaptive modules for enhancing Graph Convolutional Network (GCN) with new capabilities: 1) a Node Adaptive Parameter Learning (NAPL) module to capture node-specific patterns; 2) a Data Adaptive Graph Generation (DAGG) module to infer the inter-dependencies among different traffic series automatically. We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks. Our experiments on two real-world traffic datasets show AGCRN outperforms state-of-the-art by a significant margin without pre-defined graphs about spatial connections.

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TIST Journal 2020 Journal Article

From Appearance to Essence

Xiu Susie Fang
Quan Z. Sheng
Xianzhi Wang
Wei Emma Zhang
Anne H. H. Ngu
Jian Yang

Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp, a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.

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IS Journal 2020 Journal Article

MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation

Yang Xiao
Lina Yao
Qingqi Pei
Xianzhi Wang
Jian Yang
Quan Z. Sheng

Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated in social network services. However, most existing research either predicts users’ consumption preferences or recommends friends to users without dealing with them simultaneously. We propose a holistic approach to predict users’ preferences on friends and items jointly and thereby make better recommendations. To this end, we design a graph neural network that incorporates a mutualistic mechanism to model the mutual reinforcement relationship between users’ consumption behaviors and social behaviors. Our experiments on the two-real world datasets demonstrate the effectiveness of our approach in both social recommendation and link prediction.

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IJCAI Conference 2020 Conference Paper

Zero-Shot Object Detection via Learning an Embedding from Semantic Space to Visual Space

Licheng Zhang
Xianzhi Wang
Lina Yao
Lin Wu
Feng Zheng

Zero-shot object detection (ZSD) has received considerable attention from the community of computer vision in recent years. It aims to simultaneously locate and categorize previously unseen objects during inference. One crucial problem of ZSD is how to accurately predict the label of each object proposal, i. e. categorizing object proposals, when conducting ZSD for unseen categories. Previous ZSD models generally relied on learning an embedding from visual space to semantic space or learning a joint embedding between semantic description and visual representation. As the features in the learned semantic space or the joint projected space tend to suffer from the hubness problem, namely the feature vectors are likely embedded to an area of incorrect labels, and thus it will lead to lower detection precision. In this paper, instead, we propose to learn a deep embedding from the semantic space to the visual space, which enables to well alleviate the hubness problem, because, compared with semantic space or joint embedding space, the distribution in visual space has smaller variance. After learning a deep embedding model, we perform $k$ nearest neighbor search in the visual space of unseen categories to determine the category of each semantic description. Extensive experiments on two public datasets show that our approach significantly outperforms the existing methods.

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IJCAI Conference 2019 Conference Paper

STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

Lei Bai
Lina Yao
Salil S. Kanhere
Xianzhi Wang
Quan Z. Sheng

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.

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AAAI Conference 2019 Conference Paper

Zero-Shot Object Detection with Textual Descriptions

Zhihui Li
Lina Yao
Xiaoqin Zhang
Xianzhi Wang
Salil Kanhere
Huaxiang Zhang

Object detection is important in real-world applications. Existing methods mainly focus on object detection with sufficient labelled training data or zero-shot object detection with only concept names. In this paper, we address the challenging problem of zero-shot object detection with natural language description, which aims to simultaneously detect and recognize novel concept instances with textual descriptions. We propose a novel deep learning framework to jointly learn visual units, visual-unit attention and word-level attention, which are combined to achieve word-proposal affinity by an element-wise multiplication. To the best of our knowledge, this is the first work on zero-shot object detection with textual descriptions. Since there is no directly related work in the literature, we investigate plausible solutions based on existing zero-shot object detection for a fair comparison. We conduct extensive experiments on three challenging benchmark datasets. The extensive experimental results confirm the superiority of the proposed model.

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