activity
20162021
most citedFake News Mitigation via Point Process Based Intervention

113 citations · 373 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.LG2020★ 7 cited

GraphOpt: Learning Optimization Models of Graph Formation

Rakshit Trivedi, Jiachen Yang, Hongyuan Zha

Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging. In real-world domains, one often has access only to the f…

cs.LG2018★ 10 cited

LinkNBed: Multi-Graph Representation Learning with Entity Linkage

Rakshit Trivedi, Bunyamin Sisman, Jun Ma +3

Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowl…

cs.LG2018★ 49 cited

Representation Learning over Dynamic Graphs

Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal +1

How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learn…

cs.LG2017★ 8 cited

Learning Deep Mean Field Games for Modeling Large Population Behavior

Jiachen Yang, Xiaojing Ye, Rakshit Trivedi +2

We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete t…

cs.LG2017★ 113 cited

Fake News Mitigation via Point Process Based Intervention

Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye +6

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. Th…

cs.LG2016★ 74 cited

Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

Hanjun Dai, Yichen Wang, Rakshit Trivedi +1

Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item…