Graph Mining

Large-Scale Balanced Partitioning

Balanced Partitioning splits a large graph into roughly equal parts while minimizing cut size. The problem of "fairly" dividing a graph occurs in a number of contexts, such as assigning work in a distributed processing environment. Our techniques provided a 40% drop in multi-shard queries in the Google Maps Driving Directions, saving a significant amount of CPU usage.

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• Distributed Balanced Partitioning via Linear Embedding

Large-Scale Clustering

Our team specializes in clustering graphs at Google scale. We have efficient implementations of many different algorithms including hierarchical clustering, overlapping clustering, local clustering, and spectral clustering.

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• A Local Algorithm for Finding Well-Connected Clusters
• Affinity Clustering: Hierarchical Clustering at Scale

Large-Scale Connected Components

Connected Components is a fundamental subroutine in many graph algorithms. We have state-of-the-art implementations in a variety of paradigms including MapReduce, a distributed hash table, Pregel, and ASYMP. Our methods are 10-30x faster than the best previously studied algorithms, and easily scale to graphs with trillions of edges.

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• Connected Components in MapReduce and Beyond

Large-Scale Link Modeling

Our similarity ranking and centrality metrics serve as good features for understanding the characteristics of large graphs. They allow the development of link models useful for both link prediction and anomalous link discovery. Our tool Grale learns a similarity function that models the link relationships present in data.

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• When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks

Large-Scale Similarity Ranking

Our research in pairwise similarity ranking has produced a number of innovative methods, which we have published at top conferences such as WWW, ICML, and VLDB. We maintain a library of similarity algorithms including distributed Personalized PageRank, Egonet similarity, Adamic Adar, and others.

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• Ego-net Community Mining Applied to Friend Suggestion
• Reduce and Aggregate: Similarity Ranking in Multi-categorical Bipartite Graphs
• Research from VLDB 2016: Improved Friend Suggestion using Ego-Net Analysis
• SoK: The Evolution of Sybil Defense via Social Networks

Public-private Graph Computation

Our award-winning research on novel models of graph computation addresses important issues of privacy in graph mining. Specifically, we present techniques to efficiently solve graph problems, including computing clustering, centrality scores and shortest path distances for each node, based on its personal view of the private data in the graph while preserving the privacy of each user.

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• Efficient Algorithms for Public-Private Social Networks
• KDD 2015 Best Research Paper Award: "Algorithms for Public-Private Social Networks"

Streaming and Dynamic Graph Algorithms

We perform innovative research analyzing massive dynamic graphs. We have developed efficient algorithms for computing densest subgraph and triangle counting which operate even when subject to high velocity streaming updates.

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• Efficient Densest Subgraph Computation in Evolving Graphs
• TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size

ASYMP: Async Message Passing Graph Mining

ASYMP is graph mining framework based on asynchronous message passing. We have highly scalable code for Connected Components and shortest-path to a subset of nodes in this framework.

Large-Scale Centrality Ranking

Google’s most famous algorithm, PageRank, is a method for computing importance scores for vertices of a directed graph. In addition to PageRank, we have scalable implementations of several other centrality scores, such as harmonic centrality.

Large-Scale Graph Building

Our library GraphBuilder can convert data from a metric space (such as document text) into a similarity graph. GraphBuilder scales to massive datasets by applying fast locality sensitive hashing and neighborhood search.