Workshop
Graph Learning for Industrial Applications: Finance, Crime Detection, Medicine and Social Media
Manuela Veloso · John Dickerson · Senthil Kumar · Eren K. · Jian Tang · Jie Chen · Peter Henstock · Susan Tibbs · Ani Calinescu · Naftali Cohen · C. Bayan Bruss · Armineh Nourbakhsh
Virtual
Fri 9 Dec, 6:50 a.m. PST
Graph structures provide unique opportunities in representing complex systems that are challenging to model otherwise, due to a variety of complexities such as large number of entities, multiple entity types, different relationship types, and diverse patterns.
This provides unique opportunities in using graph and graph-based solutions within a wide array of industrial applications. In financial services,graph representations are used to model markets’ transactional systems and detect financial crime. In the healthcare field, knowledge graphs have gained traction as the best way of representing the interdisciplinary scientific knowledge across biology, chemistry, pharmacology, toxicology, and medicine. By mining scientific literature and combining it with various data sources, the knowledge graphs provide an up-to-date framework for both human and computer intelligence to generate new scientific hypotheses, drug strategies, and ideas.
In addition to the benefits of graph representation, graph native machine-learning solutions such as graph neural networks, convolutional networks, and others have been implemented effectively in many industrial systems. In finance, graph dynamics have been studied to capture emerging phenomena in volatile markets. In healthcare, these techniques have extended the traditional network analysis approaches to enable link prediction. A recent example was BenevolentAI’s knowledge graph prediction that a baricitinib (now in clinical trials), a rheumatoid arthritis drug by Eli Lily, could mitigate COVID-19’s “cytokine storm”.
Graph representations allow researchers to model inductive biases, encode domain expertise, combine explicit knowledge with latent semantics, and mine patterns at scale. This facilitates explainability, robustness, transparency, and adaptability—aspects which are all uniquely important to the financial services industry as well as the (bio)medical domain. Recent work on numeracy, tabular data modeling, multimodal reasoning, and differential analysis, increasingly rely on graph-based learning to improve performance and generalizability. Additionally, many financial datasets naturally lend themselves to graph representation—from supply-chains and shipping routes to investment networks and business hierarchies. Similarly, much of the healthcare space is best described by complex networks from the micro level of chemical synthesis protocols and biological pathways to the macro level of public health.
In recent years, knowledge graphs have shown promise in furthering the capabilities of graph representations and learning techniques with unique opportunities such as reasoning. Reasoning over knowledge graphs enables exciting possibilities in complementing the pattern detection capabilities of the traditional machine learning solutions with interpretability and reasoning potential.
This path forward highlights the importance of graphs in the future of AI and machine learning systems. This workshop highlights the current and emerging opportunities from the perspective of industrial applications such as financial services, healthcare, (bio)medicine, and crime detection. The workshop is an opportunity for academic and industrial AI researchers to come together and explore shared challenges, new topics, and emerging opportunities.
Schedule
Fri 6:50 a.m. - 7:00 a.m.
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Opening remarks
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Opening remarks
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SlidesLive Video |
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Fri 7:00 a.m. - 7:45 a.m.
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Keynote
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Keynote
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SlidesLive Video |
Craig Knoblock 🔗 |
Fri 8:15 a.m. - 8:45 a.m.
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Invited speaker
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Invited speaker
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SlidesLive Video |
Shameer Khader 🔗 |
Fri 8:45 a.m. - 9:15 a.m.
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Break
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Fri 9:15 a.m. - 9:45 a.m.
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Invited speaker
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Invited speaker
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SlidesLive Video |
Benedek Rozemberczki 🔗 |
Fri 9:45 a.m. - 10:00 a.m.
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Factor Investing with a Deep Multi-Factor Model
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Oral
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SlidesLive Video |
Zikai Wei · Bo Dai · Dahua Lin 🔗 |
Fri 10:00 a.m. - 10:15 a.m.
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Understanding stock market instability via graph auto-encoders
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Oral
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SlidesLive Video |
Dragos Gorduza · Xiaowen Dong · Stefan Zohren 🔗 |
Fri 10:15 a.m. - 10:30 a.m.
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Learning on Graphs for Mineral Asset Valuation Under Supply and Demand Uncertainty
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Oral
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SlidesLive Video |
Yassine Yaakoubi · Hager Radi · Roussos Dimitrakopoulos 🔗 |
Fri 10:30 a.m. - 10:45 a.m.
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Graph Q-Learning for Combinatorial Optimization
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Oral
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SlidesLive Video |
Victoria Magdalena Dax · Jiachen Li · Kevin Leahy · Mykel J Kochenderfer 🔗 |
Fri 10:45 a.m. - 11:00 a.m.
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Dual GNNs: Learning Graph Neural Networks with Limited Supervision
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Oral
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SlidesLive Video |
Abdullah Alchihabi · 🔗 |
Fri 11:00 a.m. - 11:45 a.m.
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Keynote
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Keynote
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SlidesLive Video |
Marinka Zitnik 🔗 |
Fri 11:45 a.m. - 12:45 p.m.
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Lunch break
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Fri 12:45 p.m. - 1:15 p.m.
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Invited speaker
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Invited speaker
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SlidesLive Video |
Yu Liu 🔗 |
Fri 1:15 p.m. - 2:00 p.m.
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Keynote
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Keynote
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SlidesLive Video |
Tucker Balch 🔗 |
Fri 2:00 p.m. - 2:30 p.m.
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Break
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Fri 2:30 p.m. - 3:00 p.m.
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Invited speaker
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Invited speaker
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SlidesLive Video |
Mohammad Ghassemi 🔗 |
Fri 3:00 p.m. - 3:12 p.m.
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Improving Protein Subcellular Localization Prediction with Structural Prediction & Graph Neural Networks
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Poster
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SlidesLive Video |
Geoffroy Dubourg-Felonneau · Arash Abbasi · Eyal Akiva · Lawrence Lee 🔗 |
Fri 3:12 p.m. - 3:24 p.m.
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Homological Neural Networks
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Poster
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SlidesLive Video |
Yuanrong Wang · Tomaso Aste 🔗 |
Fri 3:24 p.m. - 3:36 p.m.
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Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
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Poster
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SlidesLive Video |
Sagar Srinivas Sakhinana · Rajat Sarkar · Venkataramana Runkana 🔗 |
Fri 3:36 p.m. - 3:48 p.m.
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Dissecting In-the-Wild Stress from Multimodal Sensor Data
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Poster
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SlidesLive Video |
Sujay Nagaraj · Thomas Hartvigsen · Adrian Boch · Luca Foschini · Marzyeh Ghassemi · Sarah Goodday · Stephen Friend · Anna Goldenberg 🔗 |
Fri 3:48 p.m. - 4:00 p.m.
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Sample-Specific Contextualized Graphical Models Using Clinical and Molecular Data Reveal Transcriptional Network Heterogeneity Across 7000 Tumors
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Poster
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SlidesLive Video |
Caleb Ellington · Ben Lengerich · Thomas Watkins · Jiekun Yang · Manolis Kellis · Eric Xing 🔗 |
Fri 4:00 p.m. - 4:10 p.m.
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Closing remarks
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Closing remarks
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