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Call For Participation

GraphLearning 2022: International Workshop on Graph Learning
April 25, 2022, Online

http://www.graphlearning.net/

A workshop of The ACM Web Conference 2022: https://www2022.thewebconf.org/

Time Zone: Central European Summer Time (CEST, UTC+2)
Date: Monday, April 25, 2022
Time: 8:00 AM – 3:30 PM
Location: Online

[KEYNOTE TALKS]

Title: Structure-based Large-scale Dynamic Heterogeneous Graphs Processing:
Applications, Challenges and Solutions
Speaker: Prof. Wenjie Zhang, University of New South Wales, Australia

Title: Graphs in Computer Vision Then and Now: How Deep Learning has
Reinvigorated Structural Pattern Recognition
Speaker: Prof. Donatello Conte, University of Tours, France

Title: Graph Neural Networks beyond Weisfeiler-Lehman and vanilla Message
Passing
Speaker: Prof. Michael Bronstein, University of Oxford, United Kingdom


[PROGRAM]

8:00 – 8:10 Opening Remarks

8:10 – 8:50 Keynote I
Structure-based Large-scale Dynamic Heterogeneous Graphs Processing:
Applications, Challenges and Solutions
Wenjie Zhang

8:50 – 10:20 Technical Session I
Deep Partial Multiplex Network Embedding
Qifan Wang, Yi Fang, Ruining He, Anirudh Ravula, Bin Shen, Jingang Wang,
Xiaojun Quan and Dongfang Liu

Multi-view Omics Translation with Multiplex Graph Neural Networks
Costa Georgantas and Jonas Richiardi

A Triangle Framework Among Subgraph Isomorphism, pharmacophore and
structure-function relationship
Mengjiao Guo, Hui Zheng, Tengfei Ji and Jing He

CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
Yassaman Ommi, Matin Yousefabadi, Faezeh Faez, Amirmojtaba Sabour, Mahdieh
Soleymani Baghshah and Hamid R. Rabiee

Mining Multivariate Implicit Relationships in Academic Networks
Bo Xu, Bowen Chen, Tianyu Zhang, Jiaying Liu, Chunke Liao and Zhehuan Zhao

SchemaWalk: Schema Aware Random Walks for Heterogeneous Graph Embedding
Ahmed E. Samy, Lodovico Giaretta, Zekarias T. Kefato and Sarunas
Girdzijauskas

10:20 – 10:45 Break

10:45 – 11:25 Keynote II
Graphs in Computer Vision Then and Now: How Deep Learning Has Reinvigorated
Structural Pattern Recognition?
Donatello Conte

11:25 – 12:55 Technical Session II
Graph Augmentation Learning
Shuo Yu, Huafei Huang, Minh Dao and Feng Xia

Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video
Services
Fan Zhang, Qiuying Peng, Yulin Wu, Zheng Pan, Rong Zeng, Da Lin and Yue Qi

Mining Homophilic Groups of Users using Edge Attributed Node Embedding from
Enterprise Social Networks
Priyanka Sinha, Ritu Patel, Pabitra Mitra, Dilys Thomas and Lipika Dey

RePS: Relation, Position and Structure aware Entity Alignment
Anil Surisetty, Deepak Chaurasiya, Nitish Kumar, Alok Singh, Gaurav Dhama,
Aakarsh Malhotra, Vikrant Dey and Ankur Arora

Scaling R-GCN Training with Graph Summarization
Alessandro Generale, Till Blume and Michael Cochez

JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning
Selahattin Akkas and Ariful Azad

12:55 – 13:10 Break

13:10 – 13:50 Keynote III
Graph Neural Networks beyond Weisfeiler-Lehman and vanilla Message Passing
Michael Bronstein

13:50 – 15:20 Technical Session III
MarkovGNN: Graph Neural Networks on Markov Diffusion
Md. Khaledur Rahman, Abhigya Agrawal and Ariful Azad

Unsupervised Superpixel-Driven Parcel Segmentation of Remote Sensing Images
Using Graph Convolutional Network
Fulin Huang, Zhicheng Yang, Hang Zhou, Chen Du, Andy J.Y. Wong, Yuchuan
Gou, Mei Han and Jui-Hsin Lai

Improving Bundles Recommendation Coverage in Sparse Product Graphs
Saloni Agarwal, Aparupa Das Gupta and Amit Pande

Revisiting Neighborhood-based Link Prediction for Collaborative Filtering
Hao-Ming Fu, Patrick Poirson, Kwot Sin Lee and Chen Wang

Understanding Dropout for Graph Neural Networks
Juan Shu, Bowei Xi, Yu Li, Fan Wu, Charles Kamhoua and Jianzhu Ma

Surj: Ontological Learning for Fast, Accurate, and Robust Hierarchical
Multi-label Classification
Sean Yang and Bill Howe

15:20 – 15:30 Closing

[Organizers]
Feng Xia, Federation University Australia
Renaud Lambiotte, University of Oxford
Charu Aggarwal, IBM T. J. Watson Research Center

[Contact Info]
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