Submodular Variational Inference for Network Reconstruction

by   Lin Chen, et al.

In real-world and online social networks, individuals receive and transmit information in real time. Cascading information transmissions (e.g. phone calls, text messages, social media posts) may be understood as a realization of a diffusion process operating on the network, and its branching path can be represented by a directed tree. The process only traverses and thus reveals a limited portion of the edges. The network reconstruction/inference problem is to infer the unrevealed connections. Most existing approaches derive a likelihood and attempt to find the network topology maximizing the likelihood, a problem that is highly intractable. In this paper, we focus on the network reconstruction problem for a broad class of real-world diffusion processes, exemplified by a network diffusion scheme called respondent-driven sampling (RDS). We prove that under realistic and general models of network diffusion, the posterior distribution of an observed RDS realization is a Bayesian log-submodular model.We then propose VINE (Variational Inference for Network rEconstruction), a novel, accurate, and computationally efficient variational inference algorithm, for the network reconstruction problem under this model. Crucially, we do not assume any particular probabilistic model for the underlying network. VINE recovers any connected graph with high accuracy as shown by our experimental results on real-life networks.


page 1

page 2

page 3

page 4


Variational methods for simulation-based inference

We present Sequential Neural Variational Inference (SNVI), an approach t...

Variational hybridization and transformation for large inaccurate noisy-or networks

Variational inference provides approximations to the computationally int...

Variational Gaussian Process Diffusion Processes

Diffusion processes are a class of stochastic differential equations (SD...

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

Information diffusion in online social networks is affected by the under...

Experiments in Inferring Social Networks of Diffusion

Information diffusion is a fundamental process that takes place over net...

A Statistical Model for Dynamic Networks with Neural Variational Inference

In this paper we propose a statistical model for dynamically evolving ne...

Efficient reconstruction of transmission probabilities in a spreading process from partial observations

An important problem of reconstruction of diffusion network and transmis...

Please sign up or login with your details

Forgot password? Click here to reset