# A General Framework For Task-Oriented Network Inference

## TL;DR

This paper introduces a flexible framework for task-oriented network inference that models data as a network space and jointly learns network structure with influence maximization solutions.

## Contribution

It presents a formal problem statement for influence maximization where the network structure is learned jointly with the influence maximization process.

## Key findings

- Framework effectively models data as a network space
- Joint learning of network structure and influence maximization is feasible
- Formal problem statement for influence maximization without predefined network

## Abstract

We present a brief introduction to a flexible, general network inference framework which models data as a network space, sampled to optimize network structure to a particular task. We introduce a formal problem statement related to influence maximization in networks, where the network structure is not given as input, but learned jointly with an influence maximization solution.