Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Log in
Learn more about individual access
Request company access
Electric Powertrains and Energy Systems 2025 | 26 + 27 March 2025

19th International MTZ Congress on Future Powertrains

Take this opportunity to attend the conference. Experts from the automotive industry, energy providers, and grid operators will meet up with representatives from politics and science to discuss important topics for this sector.
EXTENDED SEARCH
Log in
Top
Social Network Analysis and Mining
Published in:

01-12-2020 | Original Article

Efficient influence spread estimation for influence maximization

Authors: Zahra Aghaee, Sahar Kianian

Published in: Social Network Analysis and Mining | Issue 1/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Word-of-Mouth promotion is among the effective methods of marketing and is highly regarded by many commercial companies. This type of marketing is mapped on the influence maximization problem (IMP) in the social networks, and its goal is finding a specific set of the individuals with the maximum influence on the network. Therefore, in this paper, a heuristic-greedy algorithm named the HEDVGreedy algorithm was proposed for the IMP in the social networks. In this algorithm, the expected diffusion value of the graph nodes was calculated using the heuristic method, and then, the effective nodes were selected using the greedy method. Experimental results showed that the proposed algorithm has a high performance than the baseline algorithms while, it significantly reduces the running time of the computations under both the Independent Cascade and Weighted Cascade models in the eight real-world data sets.
previous article Stability of network centrality measures: a numerical study
next article Topics extraction in incremental short texts based on LSTM

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Footnotes
Literature
Banerjee S, Jenamani M, Pratihar DK (2019) ComBIM: a community-based solution approach for the Budgeted Influence Maximization Problem. Expert Syst Appl 125:1–13CrossRef
Beni HA, Bouyer A (2020) TI-SC: top-k influential nodes selection based on community detection and scoring criteria in social networks. J Ambient Intell Humaniz Comput 1–20
Bian R et al (2019) Identifying top-k nodes in social networks: a survey. ACM Comput Surv (CSUR) 52(1):1–33CrossRef
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308CrossRef
Bucur D, Iacca G (2016) Influence maximization in social networks with genetic algorithms. In: European conference on the applications of evolutionary computation. Springer
Chang T-C et al (2019) Seed selection and social coupon allocation for redemption maximization in online social networks. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE
Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM
Cheng S et al (2013) Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM
Cui L et al (2018) DDSE: a novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks. J Netw Comput Appl 103:119–130CrossRef
da Silva AR et al (2018) Influence maximization in network by genetic algorithm on linear threshold model. In: International conference on computational science and its applications. Springer
Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM
Emami N, Mozafari N, Hamzeh A (2018) Continuous state online influence maximization in social network. Soc Netw Anal Min 8(1):32CrossRef
Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRef
Gong M et al (2016) Influence maximization in social networks based on discrete particle swarm optimization. Inf Sci 367:600–614CrossRef
Goyal A, Lu W, Lakshmanan LV (2011) Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on World wide web. ACM
Guimera R et al (2003) Self-similar community structure in a network of human interactions. Phys Rev E 68(6):065103CrossRef
He Q et al (2019) An effective scheme to address influence maximization for opinion formation in social networks. Trans Emerg Telecommun Technol 30(6):e3599
Jiang Q et al (2011) Simulated annealing based influence maximization in social networks. In: Twenty-fifth AAAI conference on artificial intelligence
Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM
Krömer P, Nowaková J (2017) Guided genetic algorithm for the influence maximization problem. In: International computing and combinatorics conference. Springer
Leskovec J et al (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM
Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data (TKDD) 1(1):2-esCrossRef
Liu D et al (2017a) A fast and efficient algorithm for mining top-k nodes in complex networks. Sci Rep 7:43330CrossRef
Liu S-J, Chen C-Y, Tsai C-W (2017b) An effective simulated annealing for influence maximization problem of online social networks. Procedia Comput Sci 113:478–483CrossRef
Ma L, Liu Y (2019) Maximizing three-hop influence spread in social networks using discrete comprehensive learning artificial bee colony optimizer. Appl Soft Comput 83:105606CrossRef
More JS, Lingam C (2019) A gradient-based methodology for optimizing time for influence diffusion in social networks. Soc Netw Anal Min 9(1):5CrossRef
Morone F et al (2016) Collective influence algorithm to find influencers via optimal percolation in massively large social media. Sci Rep 6:30062CrossRef
Newman ME (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036104MathSciNetCrossRef
Peng S et al (2018) Influence analysis in social networks: a survey. J Netw Comput Appl 106:17–32CrossRef
Rui X et al (2019) A reversed node ranking approach for influence maximization in social networks. Appl Intell 49(7):2684–2698CrossRef
Rui X et al (2020) A neighbour scale fixed approach for influence maximization in social networks. Computing 102(2):427–449MathSciNetCrossRef
Sanatkar MR (2020) The dynamics of polarized beliefs in networks governed by viral diffusion and media influence. Soc Netw Anal Min 10(1):1–21CrossRef
Saxena B, Kumar P (2019) A node activity and connectivity-based model for influence maximization in social networks. Soc Netw Anal Min 9(1):40CrossRef
Shang J et al (2017) CoFIM: a community-based framework for influence maximization on large-scale networks. Knowl-Based Syst 117:88–100CrossRef
Shang J et al (2018) IMPC: influence maximization based on multi-neighbor potential in community networks. Physica A 512:1085–1103CrossRef
Singh SS et al (2019) Mim2: multiple influence maximization across multiple social networks. Physica A 526:120902CrossRef
Tang J, Tang X, Yuan J (2018a) An efficient and effective hop-based approach for influence maximization in social networks. Soc Netw Anal Min 8(1):10CrossRef
Tang J et al (2018b) Maximizing the spread of influence via the collective intelligence of discrete bat algorithm. Knowl-Based Syst 160:88–103CrossRef
Tang J et al (2019a) An adaptive discrete particle swarm optimization for influence maximization based on network community structure. Int J Mod Phys C (IJMPC) 30(06):1–21MathSciNet
Tang J et al (2019b) Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization. Physica A 513:477–496CrossRef
Tang J et al (2020) A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks. Knowl-Based Syst 187:104833CrossRef
Tsai C-W, Liu S-J (2019) SEIM: search economics for influence maximization in online social networks. Future Gener Comput Syst 93:1055–1064CrossRef
Tsai C-W, Yang Y-C, Chiang (2015) A genetic newgreedy algorithm for influence maximization in social network. In: 2015 IEEE international conference on systems, man, and cybernetics. IEEE
Wang Q et al (2017) Discrete particle swarm optimization based influence maximization in complex networks. In: 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE
Wang W, Street WN (2018) Modeling and maximizing influence diffusion in social networks for viral marketing. Appl Netw Sci 3(1):6CrossRef
Wu K (2015) Influence maximization in social networks. Concordia University
Wu H et al (2018) LAIM: a linear time iterative approach for efficient influence maximization in large-scale networks. IEEE Access 6:44221–44234CrossRef
Zareie A, Sheikhahmadi A, Jalili M (2020) Identification of influential users in social network using gray wolf optimization algorithm. Expert Syst Appl 142:112971CrossRef
Zhu W et al (2019) Location-based seeds selection for influence blocking maximization in social networks. IEEE Access 7:27272–27287CrossRef
Metadata
Title
Efficient influence spread estimation for influence maximization
Authors
Zahra Aghaee
Sahar Kianian
Publication date
01-12-2020
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2020
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-020-00694-z

Other articles of this Issue 1/2020

A framework for improving electoral forecasting based on time-aware polling

  • Original Article

ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

  • Original Article

Discovering patterns of customer financial behavior using social media data

  • Original Article

GCNEXT: graph convolutional network with expanded balance theory for fraudulent user detection

  • Open Access
  • Original Article

AppMonitor: restricting information leakage to third-party applications

  • Original Article

Modeling the impact of Python and R packages using dependency and contributor networks

  • Original Article

Premium Partner

    Image Credits