We are experiencing an exponential growth in the number of proposed graph and machine learning solutions for a variety of problems. With explosive growth come claims and counterclaims as to which approach---graph or machine learning---is best. In some cases, old problems are recast in the alternate approach in the hope of finding a better solution; while in other cases, an approach is chosen to solve a new problem without a sound theoretical basis for success.
The conundrum is that both graph algorithms and machine learning can solve many real world problems, and that their domains intersect, but are not equivalent. For example, both community detection algorithms and SVMs partition data into subsets of similar members, and Bayesian Networks are a probabilistic graphical model of random variables and conditional dependencies used to learn causal relationships.
In reality, many analytic workloads require both approaches: graphs to understand relationships and organizational structures, and machine-learning methods to identify signature features. Given the difference in the parallel execution models of graph algorithms and machine learning methods, current tools, runtime systems, and architectures do not deliver similar performance in all cases.
The objectives of this workshop are:
- Clarify the domain of problems best solved by graphs and those best solved by machine learning approaches,
- Formulate a sound theoretical basis for choosing among approaches,
- Identify analytic workloads requiring multiple approaches, and
- Evaluate the performance and scalability of integrated platforms for graph methods and machine learning.
While there is a significant amount of interesting and critical research on the development of platforms for graphs and machine learning, and the scaling of the platforms themselves on novel and high performance systems, this workshop aspires to be more theoretical in nature, investigating their respective problem domains. The theoretical applicability aspect then naturally impacts the more practical tractability aspect, in terms of complexity, performance, and quality of solution, as they are dependent on the actual implementation of the systems frameworks (software, hardware, and combination thereof).
The workshop seeks submissions of papers in the context of graph and machine learning methods that:
- Discuss the problem domains and problems addressable with graph methods, machine learning methods, or both.
- Discuss approaches for defining problems in a way that is suitable to the application of graph methods, machine learning methods, or both
- Discuss representations that rely on concept from graph theory and algorithms, and enable the formulation of machine learning problems (e.g., Probabilistic Graphical Models)
- Identify advantages and disadvantages on the application of the methods.
- Provide tractability performance analysis in terms of complexity, time-to-solution, problem size, and quality of solution.
- Discuss integration of graphs and machine learning approaches in a mixed workflow.
- Discuss tools for graphs and machine learning algorithms, and their integration to realize such a mixed workflow