activity
20172020
most citedKnow-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

107 citations · 116 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20207 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

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

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.DL20172 cited

Tweeting about journal articles: Engagement, marketing or just gibberish?

Nicolas Robinson-Garcia, Rakshit Trivedi, Rodrigo Costas +3

This paper presents preliminary results on the analysis of tweets to journal articles in the field of Dentistry. We present two case studies in which we critically examine the cont…

cs.AI2017107 cited

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

Rakshit Trivedi, Hanjun Dai, Yichen Wang +1

The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over…