Much of Google’s algorithm development occurs in groups scattered throughout New York City. Yesterday, Google launched a single website – NYC Algorithms and Optimization Team page – to provide a deeper view into all of the NYC-based groups work.
The NYC Algorithms and Optimization Team comprises multiple overlapping research groups working on large-scale graph mining, large-scale optimization and market algorithms among other areas. Not surprisingly the work is in support of Google products and a wide variety of general challenges such as optimizing infrastructure and protecting privacy.
The new site, says Google, is intended to help it more effectively share its work and broaden its dialogue with the research and engineering community. “Please visit the site to learn about our latest projects, publications, seminars, and research areas,” write Vahab Mirrokni, Principal Research Scientist and Xerxes Dotiwalla, Product Manager, NYC Algorithms and Optimization Team, in a blogpost announcing the site.
One example is the Large-scale Graph Mining Group, which is tasked with building the most scalable library for graph algorithms and analysis and applying it to a multitude of Google products. “We formalize data mining and machine learning challenges as graph algorithms problems and perform fundamental research in those fields leading to publications in top venues,” write and Dotiwalla.
Our projects include:
- Large-scale Similarity Ranking: Our research in pairwise similarity ranking has produced a number of innovative methods, which we have published in top venues such as WWW, ICML, and VLDB, e.g., improving friend suggestion using ego-networksand computing similarity rankings in large-scale multi-categorical bipartite graphs.
- Balanced Partitioning: Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems. As our papershows, we are able to achieve a 15-25% reduction in cut size compared to state-of-the-art algorithms in the literature.
- Clustering and Connected Components: We have state-of-the-art implementations of many different algorithms including hierarchical clustering, overlapping clustering, local clustering, spectral clustering, and connected components. Our methods are 10-30x faster than the best previously studied algorithms and can scale to graphs with trillions of edges.
- Public-private Graph Computation: Our researchon novel models of graph computation based on a personal view of private data preserves the privacy of each user.
The recent blog contains substantial list of other topic areas being tackled by the NYC teams. Might be worth putting this site on your regular browsing routine.
Link to blog: https://research.googleblog.com