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Jianheng Tang

17 papers hereh-index 171.7k citations24 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author11

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.LG5
  • cs.AI1
  • cs.CG1
  • cs.DB1
  • cs.HC1
same name
  • Jianheng Tang — 14 papers, h 9
  • Jianheng Tang — 8 papers, h 6
  • Jianheng Tang — 1 paper, h 2
  • Jianheng Tang — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedRethinking Graph Neural Networks for Anomaly Detection

52 citations · 128 across the 17 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022

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…

cs.LG2022★ 52 cited

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…

cs.CL2022★ 1 cited

"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…

cs.LG2022★ 2 cited

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.…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.