University of Waterloo
There is a renewed interest in graph-based machine learning which is driven by a plethora of new graph data that are now publicly available and span a wide range of applications. For example, biological networks, brain networks, social networks, citation networks, web graphs and so on. Additionally, the new graph data are large-scale and consist of millions or billions of edges, which creates scalability issues for traditional methods.
In this mini-symposium we will bring together new perspectives on graph analytics from the fields of computer science, statistics and machine learning. In particular, we will discuss scalable optimization algorithms for large scale graph analytics problems such as community detection, organizational analytics, product recommendation in a commercial search engine, and analyst tools for fighting cybercrime. We will discuss theoretical and practical aspects of local graph clustering algorithms and generalized PageRank methods and node embedding techniques. We will discuss fundamental theoretical aspects of clustering and finally, we will cover the topics of parallel and distributed methods for graph analytics.
Meeting ID: 824 1183 0444
200 University Avenue West
Waterloo, ON N2L 3G1