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

Abstract Workshop Website
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.

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