Search Results for author: Xinran He

Found 7 papers, 2 papers with code

Network Inference from a Mixture of Diffusion Models for Fake News Mitigation

no code implementations8 Aug 2020 Karishma Sharma, Xinran He, Sungyong Seo, Yan Liu

Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics.

DynGEM: Deep Embedding Method for Dynamic Graphs

1 code implementation29 May 2018 Palash Goyal, Nitin Kamra, Xinran He, Yan Liu

The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph.

Social and Information Networks

mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

1 code implementation19 Jan 2018 Yu Shi, Fangqiu Han, Xinwei He, Xinran He, Carl Yang, Jie Luo, Jiawei Han

With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding.

Network Embedding

Learning Influence Functions from Incomplete Observations

no code implementations NeurIPS 2016 Xinran He, Ke Xu, David Kempe, Yan Liu

We establish both proper and improper PAC learnability of influence functions under randomly missing observations.

Learning and Optimization with Submodular Functions

no code implementations7 May 2015 Bharath Sankaran, Marjan Ghazvininejad, Xinran He, David Kale, Liron Cohen

Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications.

GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract

no code implementations7 Oct 2014 QI, Yu, Xinran He, Yan Liu

Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications.

Group Anomaly Detection

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