activity
20092017
most citedDetecting Strong Ties Using Network Motifs

55 citations · 81 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SI201755 cited

Detecting Strong Ties Using Network Motifs

Rahmtin Rotabi, Krishna Kamath, Jon Kleinberg +1

Detecting strong ties among users in social and information networks is a fundamental operation that can improve performance on a multitude of personalization and ranking tasks. St…

cs.SI201712 cited

Cascades: A view from Audience

Rahmtin Rotabi, Krishna Kamath, Jon Kleinberg +1

Cascades on online networks have been a popular subject of study in the past decade, and there is a considerable literature on phenomena such as diffusion mechanisms, virality, cas…

cs.SI20176 cited

When Hashes Met Wedges: A Distributed Algorithm for Finding High Similarity Vectors

Aneesh Sharma, C. Seshadhri, Ashish Goel

Finding similar user pairs is a fundamental task in social networks, with numerous applications in ranking and personalization tasks such as link prediction and tie strength detect…

cs.IR20128 cited

Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture

Gilad Mishne, Jeff Dalton, Zhenghua Li +2

We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search…

cs.DS2009

Pricing strategies for viral marketing on Social Networks

David Arthur, Rajeev Motwani, Aneesh Sharma +1

We study the use of viral marketing strategies on social networks to maximize revenue from the sale of a single product. We propose a model in which the decision of a buyer to buy…