no code implementations • IEEE Transactions on Intelligent Transportation Systems 2022 • Junchen Jin, Member, IEEE, Dingding Rong, Tong Zhang, Qingyuan Ji, Haifeng Guo, Yisheng Lv, Xiaoliang Ma, and Fei-Yue Wang
This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation.
1 code implementation • 14 Jun 2021 • Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra
We bridge two research directions on graph neural networks (GNNs), by formalizing the relation between heterophily of node labels (i. e., connected nodes tend to have dissimilar labels) and the robustness of GNNs to adversarial attacks.
1 code implementation • 14 Jan 2021 • Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences, a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles--i. e., similar functions, ties or interactions with nodes in other positions--irrespective of their distance or reachability in the network.
Network Embedding Social and Information Networks
no code implementations • 1 Jan 2021 • Xiaoshuang Li, Junchen Jin, Xiao Wang, Fei-Yue Wang
This study proposes a novel approach integrating deep Q learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC), which includes a supervised learning technique of instructing a DRL model to enhance its performance.