activity
20182022
most citedLearning Vertex Convolutional Networks for Graph Classification

2 citations · 5 across the 5 of their papers we have counts for

collaborators

12 papers

cs.CL2022

Writing Style Aware Document-level Event Extraction

Zhuo Xu, Yue Wang, Lu Bai +1

Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works…

cs.CL2020

Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

Yue Wang, Zhuo Xu, Lu Bai +5

Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research atte…

cs.SI20201 cited

A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs

Lu Bai, Lixin Cui, Edwin R. Hancock

In this paper, we develop a new graph kernel, namely the Hierarchical Transitive-Aligned kernel, by transitively aligning the vertices between graphs through a family of hierarchic…

cs.LG2019

Generative Temporal Link Prediction via Self-tokenized Sequence Modeling

Yue Wang, Chenwei Zhang, Shen Wang +4

We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future link…

q-fin.ST2019

Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis

Lu Bai, Lixin Cui, Lixiang Xu +3

In this work, we develop a novel framework to measure the similarity between dynamic financial networks, i.e., time-varying financial networks. Particularly, we explore whether the…

cs.LG2019

Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking

Yue Wang, Yao Wan, Chenwei Zhang +3

Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps pe…