Influence Maximization at Community Level: A New Challenge with Non-submodularity

Nguyen, L.D.; Zhou, Kai; Thai, My T.

doi:10.1109/icdcs.2019.00040

Cited by 10 publications

(16 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, every user on social media usually belongs to a particular group and his/her behavior is influenced by those groups, making the creation of an influence on groups of users that reap more benefits than individuals. Nguyen et al [17] aimed to find a seed set of k nodes that influences the largest number of communities and show that the group influence function is neither submodular nor subpermodular, and developed several approximation algorithms to solve this problem. The authors in [28,29] investigated the problem of Group Influence Maximization, which is to select k seed users such that the number of eventually activated groups is maximized, in which, a group becomes active if β percent of nodes in this group are influenced.…”

Section: Related Workmentioning

confidence: 99%

“…for every pair of nodes (i, j) with i = j. Denote by C(u) the group that contains node u. To determine a group is influenced or not, we extend the group influence model in [17] by scoring each node in the group based on the fact that each user has a different role in his/her group. Thus, each node u ∈ V has a cost c(u) and a score b(u).…”

Section: Problem Definitionmentioning

confidence: 99%

“…Motivated by aforementioned phenomenon, recent studies have been carried out on a general version of IM, whose objective is to maximize the number of influenced groups of users instead of the number of influenced individuals, by choosing some seed set of at most k users (see, e.g. [17,24,29,28,7]). Also, one can consider a dual problem of this problem by asking for the minimum number of seed nodes to influence a given number of groups.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Groups Influence with Minimum Cost in Social Networks

Phạm¹,

Pham²,

Duong³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This paper studies a Group Influence with Minimum cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold. As the group-influence function is neither submodular nor supermodular, theoretical bounds on the quality of solutions returned by the well-known greedy approach may not be guaranteed. To address this challenge, we propose a bi-criteria polynomial-time approximation algorithm with high certainty. At the heart of the algorithm is a novel group reachable reverse sample concept, which helps speed up the estimation of the group influence function. Finally, extensive experiments conducted on real social networks show that our proposed algorithm outperform the state-of-the-art algorithms in terms of the objective value and the running time.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Groups Influence with Minimum Cost in Social Networks

Phạm¹,

Pham²,

Duong³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, some authors studied the selection of seed nodes in a social network to influence groups of users or communities instead of individuals [36][37][38][39]. They argue that in real-world scenarios, creating impact on groups is more beneficial than the individuals in a network.…”

mentioning

confidence: 99%

“…In [38], the authors characterize the intricate relationship between diversity and efficiency, which sometimes may be at odds but may also reinforce each other. Nguyen et al [39] considered the Influence Maximization problem at the Community level problem, which found seed set of k nodes that influenced to largest number of communities. They showed that the objective function was neither sub-modular nor super-modular and proposed some approximation algorithms with provable guarantees.…”

mentioning

confidence: 99%

Influence Maximization with Priority in Online Social Networks

Pham¹,

Ha²,

Vu³

et al. 2020

Algorithms

View full text Add to dashboard Cite

The Influence Maximization (IM) problem, which finds a set of k nodes (called seedset) in a social network to initiate the influence spread so that the number of influenced nodes after propagation process is maximized, is an important problem in information propagation and social network analysis. However, previous studies ignored the constraint of priority that led to inefficient seed collections. In some real situations, companies or organizations often prioritize influencing potential users during their influence diffusion campaigns. With a new approach to these existing works, we propose a new problem called Influence Maximization with Priority (IMP) which finds out a set seed of k nodes in a social network to be able to influence the largest number of nodes subject to the influence spread to a specific set of nodes U (called priority set) at least a given threshold T in this paper. We show that the problem is NP-hard under well-known IC model. To find the solution, we propose two efficient algorithms, called Integrated Greedy (IG) and Integrated Greedy Sampling (IGS) with provable theoretical guarantees. IG provides a 1−(1−1k)t-approximation solution with t is an outcome of algorithm and t≥1. The worst-case approximation ratio is obtained when t=1 and it is equal to 1/k. In addition, IGS is an efficient randomized approximation algorithm based on sampling method that provides a 1−(1−1k)t−ϵ-approximation solution with probability at least 1−δ with ϵ>0,δ∈(0,1) as input parameters of the problem. We conduct extensive experiments on various real networks to compare our IGS algorithm to the state-of-the-art algorithms in IM problem. The results indicate that our algorithm provides better solutions interns of influence on the priority sets when approximately give twice to ten times higher than threshold T while running time, memory usage and the influence spread also give considerable results compared to the others.

show abstract

Threshold Benefit for Groups Influence in Online Social Networks

Phạm

Nguyen

et al. 2021

Future Data and Security Engineering

View full text Add to dashboard Cite

Influence Maximization at Community Level: A New Challenge with Non-submodularity

Cited by 10 publications

References 19 publications

Groups Influence with Minimum Cost in Social Networks

Groups Influence with Minimum Cost in Social Networks

Influence Maximization with Priority in Online Social Networks

Threshold Benefit for Groups Influence in Online Social Networks

Contact Info

Product

Resources

About