Influence maximization in social graphs based on community structure and node coverage gain

2021 ◽  
Vol 118 ◽  
pp. 327-338
Author(s):  
Zhixiao Wang ◽  
Chengcheng Sun ◽  
Jingke Xi ◽  
Xiaocui Li
Keyword(s):  
Community Structure ◽  
Influence Maximization ◽  
Social Graphs

scholarly journals Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

2020 ◽  
Vol 2020 (4) ◽  
pp. 131-152 ◽  
Author(s):  
Xihui Chen ◽  
Sjouke Mauw ◽  
Yunior Ramírez-Cruz
Keyword(s):  
Community Structure ◽  
Differential Privacy ◽  
Original Graph ◽  
Attributed Graph ◽  
Attributed Graphs ◽  
Social Graphs ◽  
Private Computation ◽  
Novel Method ◽  
Publishing Community ◽  
New Community

AbstractWe present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM, a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release communitypreserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients.

Distributed Generation of Billion-node Social Graphs with Overlapping Community Structure

2014 ◽  
pp. 199-208 ◽  
Author(s):  
Kyrylo Chykhradze ◽  
Anton Korshunov ◽  
Nazar Buzun ◽  
Roman Pastukhov ◽  
Nikolay Kuzyurin ◽  
...  
Keyword(s):  
Community Structure ◽  
Distributed Generation ◽  
Social Graphs ◽  
Overlapping Community

IM-ELPR: Influence maximization in social networks using label propagation based community structure

2021 ◽  
Author(s):  
Sanjay Kumar ◽  
Lakshay Singhla ◽  
Kshitij Jindal ◽  
Khyati Grover ◽  
B. S. Panda
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Influence Maximization ◽  
Label Propagation

scholarly journals Community-based framework for influence maximization problem in social networks

2021 ◽  
Vol 24 (3) ◽  
pp. 1604
Author(s):  
Mustafa K. Alasadi ◽  
Ghusoon Idan Arb
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Diffusion Process ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Community Diversity ◽  
Maximization Problem ◽  
Special Role ◽  
The Social ◽  
Influence Maximization Problem

<p>Given a social graph, the influence maximization problem (IMP) is the act of selecting a group of nodes that cause maximum influence if they are considered as seed nodes of a diffusion process. IMP is an active research area in social network analysis due to its practical need in applications like viral marketing, target advertisement, and recommendation system. In this work, we propose an efficient solution for IMP based on the social network structure. The community structure is a property of real-world graphs. In fact, communities are often overlapping because of the involvement of users in many groups (family, workplace, and friends). These users are represented by overlapped nodes in the social graphs and they play a special role in the information diffusion process. This fact prompts us to propose a solution framework consisting of three phases: firstly, the community structure is discovered, secondly, the candidate seeds are generated, then lastly the set of final seed nodes are selected. The aim is to maximize the influence with the community diversity of influenced users. The study was validated using synthetic as well as real social network datasets. The experimental results show improvement over baseline methods and some important conclusions were reported.</p>

An adaptive discrete particle swarm optimization for influence maximization based on network community structure

2019 ◽  
Vol 30 (06) ◽  
pp. 1950050 ◽  
Author(s):  
Jianxin Tang ◽  
Ruisheng Zhang ◽  
Yabing Yao ◽  
Zhili Zhao ◽  
Baoqiang Chai ◽  
...  
Keyword(s):  
Social Networks ◽  
Particle Swarm Optimization ◽  
Community Structure ◽  
Particle Swarm ◽  
Influence Maximization ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Network Community ◽  
Influential Nodes

As an important research field of social network analysis, influence maximization problem is targeted at selecting a small group of influential nodes such that the spread of influence triggered by the seed nodes will be maximum under a given propagation model. It is yet filled with challenging research topics to develop effective and efficient algorithms for the problem especially in large-scale social networks. In this paper, an adaptive discrete particle swarm optimization (ADPSO) is proposed based on network topology for influence maximization in community networks. According to the framework of ADPSO, community structures are detected by label propagation algorithm in the first stage, then dynamic encoding mechanism for particle individuals and discrete evolutionary rules for the swarm are conceived based on network community structure for the meta-heuristic optimization algorithm to identify the allocated number of influential nodes within different communities. To expand the seed nodes reasonably, a local influence preferential strategy is presented to allocate the number of candidate nodes to each community according to its marginal gain. The experimental results on six social networks demonstrate that the proposed ADPSO can achieve comparable influence spread to CELF in an efficient way.

scholarly journals Correction: A Novel Top-k Strategy for Influence Maximization in Complex Networks with Community Structure

PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0162066 ◽  
Author(s):  
Jia-Lin He ◽  
Yan Fu ◽  
Duan-Bing Chen
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Influence Maximization

scholarly journals Influence Maximization on Social Graphs: A Survey

2018 ◽  
Vol 30 (10) ◽  
pp. 1852-1872 ◽  
Author(s):  
Yuchen Li ◽  
Ju Fan ◽  
Yanhao Wang ◽  
Kian-Lee Tan
Keyword(s):  
Influence Maximization ◽  
Social Graphs

scholarly journals A Novel Top-k Strategy for Influence Maximization in Complex Networks with Community Structure

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0145283 ◽  
Author(s):  
Jia-Lin He ◽  
Yan Fu ◽  
Duan-Bing Chen
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Influence Maximization
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