Influence Maximization in Social Networks using Discretized Harris Hawks Optimization Algorithm and Neighbour Scout Strategy Article Swipe
Inder Khatri
,
Arjun Choudhry
,
Aryaman Rao
,
Aryan Tyagi
,
Dinesh Kumar Vishwakarma
,
Mukesh Prasad
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.09683
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.09683
Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing, and helps significantly in social media advertising. However, developing effective algorithms with minimal time complexity for real-world social networks still remains a challenge. While traditional heuristic approaches have been applied for IM, they often result in minimal performance gains over the computationally expensive Greedy-based and Reverse Influence Sampling-based approaches. In this paper, we propose the discretization of the nature-inspired Harris Hawks Optimisation meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris Hawks intelligence, we employ a neighbour scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace. We evaluate the efficacy of our proposed DHHO approach on six social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches.
Related Topics
Concepts
Computer science
Heuristic
Maximization
Mathematical optimization
Viral marketing
Discretization
Convergence (economics)
Initialization
Greedy algorithm
Machine learning
Population
Artificial intelligence
Algorithm
Mathematics
Social media
Demography
Mathematical analysis
World Wide Web
Economic growth
Sociology
Economics
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.09683
- https://arxiv.org/pdf/2211.09683
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309397540
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4309397540Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.09683Digital Object Identifier
- Title
-
Influence Maximization in Social Networks using Discretized Harris Hawks Optimization Algorithm and Neighbour Scout StrategyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-17Full publication date if available
- Authors
-
Inder Khatri, Arjun Choudhry, Aryaman Rao, Aryan Tyagi, Dinesh Kumar Vishwakarma, Mukesh PrasadList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.09683Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2211.09683Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2211.09683Direct OA link when available
- Concepts
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Computer science, Heuristic, Maximization, Mathematical optimization, Viral marketing, Discretization, Convergence (economics), Initialization, Greedy algorithm, Machine learning, Population, Artificial intelligence, Algorithm, Mathematics, Social media, Demography, Mathematical analysis, World Wide Web, Economic growth, Sociology, Economics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.than | 186, 200 |
| abstract_inverted_index.that | 180 |
| abstract_inverted_index.they | 65 |
| abstract_inverted_index.this | 83 |
| abstract_inverted_index.time | 46 |
| abstract_inverted_index.with | 44 |
| abstract_inverted_index.Hawks | 93, 113 |
| abstract_inverted_index.While | 56 |
| abstract_inverted_index.avoid | 123 |
| abstract_inverted_index.gains | 71 |
| abstract_inverted_index.helps | 34 |
| abstract_inverted_index.media | 38 |
| abstract_inverted_index.model | 174 |
| abstract_inverted_index.nodes | 11, 105, 146 |
| abstract_inverted_index.often | 66 |
| abstract_inverted_index.scout | 119 |
| abstract_inverted_index.still | 52 |
| abstract_inverted_index.these | 144 |
| abstract_inverted_index.using | 21, 97, 170 |
| abstract_inverted_index.viral | 31 |
| abstract_inverted_index.Harris | 92, 112 |
| abstract_inverted_index.across | 193 |
| abstract_inverted_index.better | 185, 199 |
| abstract_inverted_index.employ | 116 |
| abstract_inverted_index.entire | 155 |
| abstract_inverted_index.hawks. | 132 |
| abstract_inverted_index.model. | 24 |
| abstract_inverted_index.paper, | 84 |
| abstract_inverted_index.random | 139 |
| abstract_inverted_index.result | 67 |
| abstract_inverted_index.social | 14, 37, 50, 168 |
| abstract_inverted_index.spread | 20 |
| abstract_inverted_index.Cascade | 173 |
| abstract_inverted_index.Reverse | 78 |
| abstract_inverted_index.ability | 129 |
| abstract_inverted_index.applied | 62 |
| abstract_inverted_index.enhance | 126 |
| abstract_inverted_index.minimal | 45, 69 |
| abstract_inverted_index.network | 15 |
| abstract_inverted_index.observe | 179 |
| abstract_inverted_index.optimal | 9, 101 |
| abstract_inverted_index.problem | 29 |
| abstract_inverted_index.process | 152 |
| abstract_inverted_index.propose | 86 |
| abstract_inverted_index.remains | 53 |
| abstract_inverted_index.spread. | 108 |
| abstract_inverted_index.Further, | 133 |
| abstract_inverted_index.However, | 40 |
| abstract_inverted_index.addition | 110 |
| abstract_inverted_index.approach | 165 |
| abstract_inverted_index.efficacy | 160 |
| abstract_inverted_index.evaluate | 158 |
| abstract_inverted_index.maximize | 17 |
| abstract_inverted_index.metrics, | 195 |
| abstract_inverted_index.networks | 51, 169 |
| abstract_inverted_index.performs | 197 |
| abstract_inverted_index.proposed | 163 |
| abstract_inverted_index.strategy | 120 |
| abstract_inverted_index.Influence | 0, 79, 191 |
| abstract_inverted_index.algorithm | 96, 121 |
| abstract_inverted_index.approach, | 142 |
| abstract_inverted_index.blindness | 124 |
| abstract_inverted_index.candidate | 137, 145 |
| abstract_inverted_index.community | 98 |
| abstract_inverted_index.competing | 187, 201 |
| abstract_inverted_index.effective | 42 |
| abstract_inverted_index.expensive | 75 |
| abstract_inverted_index.heuristic | 58, 202 |
| abstract_inverted_index.influence | 19, 107 |
| abstract_inverted_index.neighbour | 118 |
| abstract_inverted_index.populace. | 156 |
| abstract_inverted_index.prominent | 28 |
| abstract_inverted_index.searching | 128 |
| abstract_inverted_index.selection | 102 |
| abstract_inverted_index.algorithms | 43 |
| abstract_inverted_index.approaches | 59, 189 |
| abstract_inverted_index.challenge. | 55 |
| abstract_inverted_index.comparable | 183 |
| abstract_inverted_index.complexity | 47 |
| abstract_inverted_index.developing | 41 |
| abstract_inverted_index.diffusion. | 177 |
| abstract_inverted_index.marketing, | 32 |
| abstract_inverted_index.noticeably | 198 |
| abstract_inverted_index.population | 140 |
| abstract_inverted_index.real-world | 49 |
| abstract_inverted_index.structures | 99 |
| abstract_inverted_index.Independent | 172 |
| abstract_inverted_index.approaches. | 81, 203 |
| abstract_inverted_index.convergence | 151 |
| abstract_inverted_index.determining | 7 |
| abstract_inverted_index.influential | 10 |
| abstract_inverted_index.information | 176 |
| abstract_inverted_index.nodes-based | 138 |
| abstract_inverted_index.performance | 70 |
| abstract_inverted_index.propagation | 23 |
| abstract_inverted_index.traditional | 57 |
| abstract_inverted_index.Greedy-based | 76 |
| abstract_inverted_index.Maximization | 1, 192 |
| abstract_inverted_index.Optimisation | 94 |
| abstract_inverted_index.accelerating | 149 |
| abstract_inverted_index.advertising. | 39 |
| abstract_inverted_index.intelligence, | 114 |
| abstract_inverted_index.significantly | 35 |
| abstract_inverted_index.Sampling-based | 80 |
| abstract_inverted_index.discretization | 88 |
| abstract_inverted_index.initialization | 141 |
| abstract_inverted_index.meta-heuristic | 95, 188 |
| abstract_inverted_index.computationally | 74 |
| abstract_inverted_index.nature-inspired | 91 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |