289 citations · 400 across the 9 of their papers we have counts for
10 papers
Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
Karishma Sharma, Xinran He, Sungyong Seo +1
The dissemination of fake news intended to deceive people, influence public opinion and manipulate social outcomes, has become a pressing problem on social media. Moreover, informa…
DynGEM: Deep Embedding Method for Dynamic Graphs
Palash Goyal, Nitin Kamra, Xinran He +1
Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node clas…
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
Yu Shi, Fangqiu Han, Xinwei He +4
Multi-view networks are broadly present in real-world applications. In the meantime, network embedding has emerged as an effective representation learning approach for networked da…
Learning Influence Functions from Incomplete Observations
Xinran He, Ke Xu, David Kempe +1
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most (online and real-world)…
Robust Influence Maximization
Xinran He, David Kempe
Uncertainty about models and data is ubiquitous in the computational social sciences, and it creates a need for robust social network algorithms, which can simultaneously provide g…
Learning and Optimization with Submodular Functions
Bharath Sankaran, Marjan Ghazvininejad, Xinran He +2
In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In ca…