Adaptive Influence Maximization in Dynamic Social Networks Article Swipe
Guangmo Tong
,
Weili Wu
,
Shaojie Tang
,
Ding‐Zhu Du
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1109/tnet.2016.2563397
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1109/tnet.2016.2563397
For the purpose of propagating information and ideas through a social\nnetwork, a seeding strategy aims to find a small set of seed users that are\nable to maximize the spread of the influence, which is termed as influence\nmaximization problem. Despite a large number of works have studied this\nproblem, the existing seeding strategies are limited to the static social\nnetworks. In fact, due to the high speed data transmission and the large\npopulation of participants, the diffusion processes in real-world social\nnetworks have many aspects of uncertainness. Unfortunately, as shown in the\nexperiments, in such cases the state-of-art seeding strategies are pessimistic\nas they fails to trace the dynamic changes in a social network. In this paper,\nwe study the strategies selecting seed users in an adaptive manner. We first\nformally model the Dynamic Independent Cascade model and introduce the concept\nof adaptive seeding strategy. Then based on the proposed model, we show that a\nsimple greedy adaptive seeding strategy finds an effective solution with a\nprovable performance guarantee. Besides the greedy algorithm an efficient\nheuristic algorithm is provided in order to meet practical requirements.\nExtensive experiments have been performed on both the real-world networks and\nsynthetic power-law networks. The results herein demonstrate the superiority of\nthe adaptive seeding strategies to other standard methods.\n
Related Topics
Concepts
Computer science
Maximization
TRACE (psycholinguistics)
Greedy algorithm
Seeding
Mathematical optimization
Heuristic
Set (abstract data type)
Social network (sociolinguistics)
Population
Adaptive strategies
Artificial intelligence
Algorithm
Mathematics
Engineering
Social media
Aerospace engineering
Demography
Linguistics
Philosophy
Programming language
History
World Wide Web
Archaeology
Sociology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnet.2016.2563397
- OA Status
- green
- Cited By
- 210
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3122282375
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3122282375Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tnet.2016.2563397Digital Object Identifier
- Title
-
Adaptive Influence Maximization in Dynamic Social NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-05-24Full publication date if available
- Authors
-
Guangmo Tong, Weili Wu, Shaojie Tang, Ding‐Zhu DuList of authors in order
- Landing page
-
https://doi.org/10.1109/tnet.2016.2563397Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1506.06294Direct OA link when available
- Concepts
-
Computer science, Maximization, TRACE (psycholinguistics), Greedy algorithm, Seeding, Mathematical optimization, Heuristic, Set (abstract data type), Social network (sociolinguistics), Population, Adaptive strategies, Artificial intelligence, Algorithm, Mathematics, Engineering, Social media, Aerospace engineering, Demography, Linguistics, Philosophy, Programming language, History, World Wide Web, Archaeology, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
210Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 19, 2023: 25, 2022: 20, 2021: 25Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.practical | 170 |
| abstract_inverted_index.processes | 73 |
| abstract_inverted_index.selecting | 113 |
| abstract_inverted_index.strategy. | 134 |
| abstract_inverted_index.guarantee. | 156 |
| abstract_inverted_index.influence, | 31 |
| abstract_inverted_index.methods.\n | 197 |
| abstract_inverted_index.paper,\nwe | 109 |
| abstract_inverted_index.real-world | 75, 179 |
| abstract_inverted_index.strategies | 50, 93, 112, 193 |
| abstract_inverted_index.Independent | 125 |
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| abstract_inverted_index.concept\nof | 131 |
| abstract_inverted_index.demonstrate | 187 |
| abstract_inverted_index.experiments | 172 |
| abstract_inverted_index.information | 5 |
| abstract_inverted_index.performance | 155 |
| abstract_inverted_index.propagating | 4 |
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| abstract_inverted_index.transmission | 65 |
| abstract_inverted_index.participants, | 70 |
| abstract_inverted_index.Unfortunately, | 82 |
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| abstract_inverted_index.this\nproblem, | 46 |
| abstract_inverted_index.uncertainness. | 81 |
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| abstract_inverted_index.large\npopulation | 68 |
| abstract_inverted_index.social\nnetworks. | 56 |
| abstract_inverted_index.the\nexperiments, | 86 |
| abstract_inverted_index.efficient\nheuristic | 162 |
| abstract_inverted_index.influence\nmaximization | 36 |
| abstract_inverted_index.requirements.\nExtensive | 171 |
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| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.99699402 |
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| citation_normalized_percentile.is_in_top_10_percent | True |