Learning Parameters for Balanced Index Influence Maximization

12/15/2020
by   Manqing Ma, et al.
0

Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine the outcome of influence. This problem is NP-hard and it has generated a significant amount of recent research on finding efficient heuristics. We focus on a Balance Index algorithm that relies on three parameters to tune its performance to the given network structure. We propose using a supervised machine-learning approach for such tuning. We select the most influential graph features for the parameter tuning. Then, using random-walk-based graph-sampling, we create small snapshots from the given synthetic and large-scale real-world networks. Using exhaustive search, we find for these snapshots the high accuracy values of BI parameters to use as a ground truth. Then, we train our machine-learning model on the snapshots and apply this model to the real-word network to find the best BI parameters. We apply these parameters to the sampled real-world network to measure the quality of the sets of initiators found this way. We use various real-world networks to validate our approach against other heuristic.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/09/2023

Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach

Maximizing influences in complex networks is a practically important but...
research
06/18/2019

DISCO: Influence Maximization Meets Network Embedding and Deep Learning

Since its introduction in 2003, the influence maximization (IM) problem ...
research
08/10/2021

Learning to Maximize Influence

As the field of machine learning for combinatorial optimization advances...
research
06/09/2019

Factorization Bandits for Online Influence Maximization

We study the problem of online influence maximization in social networks...
research
09/14/2022

Voting-based Opinion Maximization

We investigate the novel problem of voting-based opinion maximization in...
research
08/01/2022

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

Influence maximization aims to select k most-influential vertices or see...
research
09/04/2023

Dynamical Stability of Threshold Networks over Undirected Signed Graphs

In this paper we study the dynamic behavior of threshold networks on und...

Please sign up or login with your details

Forgot password? Click here to reset