Machine Learning on Graphs (MLoG) Workshop

About

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today’s world we are generating and gathering data in a much faster and diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way.

MLoG at WSDM'22 provides a venue to gather the academia researchers and industry researchers/practitioners to present the recent progress of machine learning on graphs.

Topics of Interest

Graphs are a kind of universal data structure for representing pair-wise relationships between entities, which can be ubiquitously observed in different domains ranging from computer science, social science, physics, chemistry, to biology. Many real-world applications can be treated as computational tasks on graphs. For example, friend recommendation in social networks can be regarded as a link prediction task and predicting properties of chemical compounds can be treated as a graph classification task. To facilitate these tasks, machine learning techniques have been widely adopted to perform analysis. As our ability of generating and collecting data constantly increasing in a unprecedented way, the graph-structure data we are facing in the modern era (especially coming from the web) are becoming more and more diverse, complex and large-scale. Hence, more efforts are required for developing more effective algorithms and deploying them efficiently for real-world applications. In this workshop, we aim to discuss the recent research progress of machine learning on graphs in both theoretical foundations and practical applications. We invite submissions that focus on recent advances in research/development of machine learning on graphs along with their applications.

Theory and methodology papers are welcome from any of the following areas, including but not limited to:

Graph Kernels
Graph Summarization
Graph Coarsening
Graph Alignment
Graph Generative Models
Graph Mining
Graph Neural Networks
Network Embedding
Machine Learning for Graph Combinatorial Optimization
Graph Feature Engineering and Selection
Scalable Graph Learning Models and Methods

and application papers focused on but not limited to:

Recommender Systems
Computer Vision
Natural Language Processing
Bioinformatics (e.g., drug discovery)
Cybersecurity (e.g., malware detection/propagation)
Financial security (e.g., fraudster detection)
Transportation/mobility networks (e.g., traffic prediction)
Graph ML Platforms and Systems (e.g., in-database machine learning)

Submission Details

We invite both long research papers (5-10 pages) and short research/application papers (2-4 pages) including references. All submissions must be in PDF format and formatted according to the new ACM format published in ACM guidelines (e.g., using the ACM LaTeX template on Overleaf here) and selecting the "sigconf" sample. Following the WSDM conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted works will be assessed based on their novelty, technical quality, potential impact, and clarity of writing (and should be in English). For papers that primarily rely on empirical evaluations, the experimental settings and results should be clearly presented and repeatable. We encourage authors to make data and code available publicly when possible. Accepted papers will be posted on this workshop website and will not appear in the WSDM proceedings and are thus non-archival (allowing you to submit works to MLoG at WSDM'22 even if they are current under review elsewhere). The best paper (according to the reviewers' ratings) will be announced at the end of the workshop.

All submissions must be uploaded electronically to EasyChair at: Submission Page

At least one of the authors of the accepted workshop papers must register for the workshop and be present on the day of the workshop.

For questions regarding submissions, please contact us at: wsdm2022mlog@easychair.org

Workshop Program

Accepted Papers

Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs.
Gavin Edwards, Sebastian Nilsson, Benedek Rozemberczki and Eliseo Papa

Biological Insights Knowledge Graph: an Integrated Knowledge Graph to Support Drug Development.
David Geleta, Andriy Nikolov, Gavin Edwards, Anna Gogleva, Richard Jackson, Erik Jansson, Andrej Lamov, Sebastian Nilsson, Marina Pettersson, Vladimir Poroshin, Benedek Rozemberczki, Timothy Scrivener, Michael Ughetto and Eliseo Papa
***Best Paper Award***

Simplicial Complex Representation Learning.
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Vasileios Maroulas and Xuanting Cai

Lightweight Compositional Embeddings for Incremental Streaming Recommendation.
Mengyue Hang, Tobias Schnabel, Longqi Yang and Jennifer Neville

Providing Node-level Local Explanation for noded2vec through Reinforcement Learning.
Hyunju Kang and Hogun Park

Time Evolving Spatio0Temporal Hypergraph for Stock Forecasting.
Ramit Sawhney, Shivam Agarwal, Megh Thakkar, Vineet Malhotra and Sudheer Chava

Adversarial Generation of Multi-View Tensor Graphs: A Preliminary Model and Encouraging Results.
William Shiao, Benjamin A. Miller, Kevin Chan, Paul Yu, Tina Eliassi-Rad and Evangelos E. Papalexakis

Two-view Graph Neural Networks for Knowledge Graph Completion.
Vinh Tong, Dai Quoc Nguyen, Dinh Phung and Dat Quoc Nguyen

Imbalanced Graph Classification via Graph-of-Graph Neural Networks.
Yu Wang, Yuying Zhao, Neil Shah and Tyler Derr

Workshop Program. (Local time MST in Phoenix, AZ at WSDM'22).

Morning Session (MST)

08:30 - 08:40 Welcome and Opening Remarks

08:40 - 09:15 Keynote - Yinglong Xia of Meta AI
09:15 - 09:50 Keynote - Neil Shah of Snap Inc.
09:50 - 10:25 Keynote - Xin Luna Dong of Meta

10:25 - 10:40 Coffee break/Social Networking

10:40 - 11:15 Keynote - Jingrui He of UIUC
11:15 - 11:50 Keynote - Dan McCreary of Optum
11:50 - 11:55 Short break
11:55 - 12:30 Keynote - Jay Yu of TigerGraph

Lunch Break (12:30 - 13:30)

Afternoon Session (MST)

13:30 - 14:05 Keynote - Leman Akoglu of CMU
14:05 - 14:40 Keynote - Meng Jiang of University of Notre Dame

14:40 - 15:00 Coffee break/Social Networking

15:00 - 15:35 Keynote - Yizhou Sun of UCLA
15:35 - 15:55 Future Directions Panel (Keynote Speakers)
15:55 - 16:00 Short break
16:00 - 16:25 Contributed Research Lightning Talks

16:25 - 16:30 Best Paper Award Ceremony & Final Remarks
16:30 - 17:00 Contributed Research Poster Session