52 citations · 128 across the 17 of their papers we have counts for
17 papers
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
Jianheng Tang, Fengrui Hua, Ziqi Gao +2
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a st…
A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
Jianheng Tang, Kangfei Zhao, Jia Li
Entity alignment is the task of identifying corresponding entities across different knowledge graphs (KGs). Although recent embedding-based entity alignment methods have shown sign…
Data Imputation from the Perspective of Graph Dirichlet Energy
Weiqi Zhang, Guanlue Li, Jianheng Tang +2
Data imputation is a crucial task due to the widespread occurrence of missing data. Many methods adopt a two-step approach: initially crafting a preliminary imputation (the "draft"…
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
Jiajin Li, Jianheng Tang, Lemin Kong +4
In this work, we present the Bregman Alternating Projected Gradient (BAPG) method, a single-loop algorithm that offers an approximate solution to the Gromov-Wasserstein (GW) distan…
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
Jianheng Tang, Weiqi Zhang, Jiajin Li +3
Graph alignment, which aims at identifying corresponding entities across multiple networks, has been widely applied in various domains. As the graphs to be aligned are usually cons…
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Jianheng Tang, Kejia Fan, Wenxuan Xie +6
The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies either assume that the qualities of workers are known in advance, o…
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Ziqi Gao, Yifan Niu, Jiashun Cheng +6
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation…
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang, Jiajin Li, Ziqi Gao +1
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first…
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
Wenge Liu, Yi Cheng, Hao Wang +6
With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches tha…
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
Jiajin Li, Jianheng Tang, Lemin Kong +4
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.…
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks
Jinghui Qin, Xiaodan Liang, Yining Hong +2
Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasona…
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Jiaqi Chen, Jianheng Tang, Jinghui Qin +4
Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a…
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Shuai Lin, Pan Zhou, Xiaodan Liang +4
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded di…
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
Wenge Liu, Jianheng Tang, Yi Cheng +3
Developing conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially…
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Jinghui Qin, Zheng Ye, Jianheng Tang +1
Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversatio…
Target-Guided Open-Domain Conversation
Jianheng Tang, Tiancheng Zhao, Chenyan Xiong +3
Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the pr…
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
Lin Xu, Qixian Zhou, Ke Gong +3
Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medica…