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.
* 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.
User Name : jerish
Posted 23-11-2016 on 10:01:24 AEDT