# Promoting information spreading by using contact memory

## TL;DR

This paper introduces a non-Markovian model for information spreading that incorporates human memory, revealing an effective contact strategy that enhances spreading efficiency on various networks.

## Contribution

It proposes a novel contact strategy based on contact memory, improving understanding of spreading dynamics and providing a mean-field theory that aligns with simulations.

## Key findings

- Preferential contact with less-contacted neighbors promotes spreading.
- High-degree nodes are targeted early, small-degree nodes later.
- The mean-field theory accurately predicts spreading behavior.

## Abstract

Promoting information spreading is a booming research topic in network science community. However, the exiting studies about promoting information spreading seldom took into account the human memory, which plays an important role in the spreading dynamics. In this paper we propose a non-Markovian information spreading model on complex networks, in which every informed node contacts a neighbor by using the memory of neighbor's accumulated contact numbers in the past. We systematically study the information spreading dynamics on uncorrelated configuration networks and a group of $22$ real-world networks, and find an effective contact strategy of promoting information spreading, i.e., the informed nodes preferentially contact neighbors with small number of accumulated contacts. According to the effective contact strategy, the high degree nodes are more likely to be chosen as the contacted neighbors in the early stage of the spreading, while in the late stage of the dynamics, the nodes with small degrees are preferentially contacted. We also propose a mean-field theory to describe our model, which qualitatively agrees well with the stochastic simulations on both artificial and real-world networks.