Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features Article Swipe
Anh-Thu Nguyen-Thi
,
Phuc Q. Nguyen
,
Thanh Duc Ngo
,
Tu-Anh Nguyen-Hoang
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.08.135
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.08.135
In signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer AdaBoost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2015.08.135
- OA Status
- diamond
- Cited By
- 9
- References
- 35
- Related Works
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- OpenAlex ID
- https://openalex.org/W1205700884
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W1205700884Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2015.08.135Digital Object Identifier
- Title
-
Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR FeaturesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2015Year of publication
- Publication date
-
2015-01-01Full publication date if available
- Authors
-
Anh-Thu Nguyen-Thi, Phuc Q. Nguyen, Thanh Duc Ngo, Tu-Anh Nguyen-HoangList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2015.08.135Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procs.2015.08.135Direct OA link when available
- Concepts
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Computer science, AdaBoost, Support vector machine, Boosting (machine learning), Artificial intelligence, Machine learning, Transfer of learning, Classifier (UML), Social network (sociolinguistics), Social media, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 1, 2020: 2, 2019: 2, 2017: 2Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.edge | 84 |
| abstract_inverted_index.etc) | 53 |
| abstract_inverted_index.fan, | 51 |
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| abstract_inverted_index.must | 25 |
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| abstract_inverted_index.sign | 85 |
| abstract_inverted_index.that | 173 |
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| abstract_inverted_index.what | 17 |
| abstract_inverted_index.whom | 15 |
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| abstract_inverted_index.(PNR) | 153 |
| abstract_inverted_index.(TAS) | 123 |
| abstract_inverted_index.Ratio | 152 |
| abstract_inverted_index.Wiki) | 169 |
| abstract_inverted_index.based | 157 |
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| abstract_inverted_index.focus | 63 |
| abstract_inverted_index.like, | 52 |
| abstract_inverted_index.links | 33, 49, 56, 71 |
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| abstract_inverted_index.novel | 113 |
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| abstract_inverted_index.study | 43 |
| abstract_inverted_index.three | 164 |
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| abstract_inverted_index.train | 95 |
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| abstract_inverted_index.which | 28, 124, 155 |
| abstract_inverted_index.RBFSVM | 133 |
| abstract_inverted_index.amount | 82 |
| abstract_inverted_index.become | 21 |
| abstract_inverted_index.called | 118 |
| abstract_inverted_index.faster | 188 |
| abstract_inverted_index.method | 172, 186 |
| abstract_inverted_index.model, | 79 |
| abstract_inverted_index.paper, | 101 |
| abstract_inverted_index.signed | 1, 44, 74, 89 |
| abstract_inverted_index.social | 2, 36, 45, 75 |
| abstract_inverted_index.content | 6 |
| abstract_inverted_index.extends | 125 |
| abstract_inverted_index.improve | 175 |
| abstract_inverted_index.kernel) | 138 |
| abstract_inverted_index.methods | 27 |
| abstract_inverted_index.network | 37, 90 |
| abstract_inverted_index.predict | 29, 67 |
| abstract_inverted_index.present | 111 |
| abstract_inverted_index.results | 162 |
| abstract_inverted_index.theory. | 160 |
| abstract_inverted_index.AdaBoost | 120 |
| abstract_inverted_index.Negative | 151 |
| abstract_inverted_index.Positive | 150 |
| abstract_inverted_index.Slashdot | 167 |
| abstract_inverted_index.Transfer | 119 |
| abstract_inverted_index.accuracy | 178 |
| abstract_inverted_index.adequate | 93 |
| abstract_inverted_index.baseline | 182 |
| abstract_inverted_index.designed | 132 |
| abstract_inverted_index.dislike, | 59 |
| abstract_inverted_index.explicit | 146 |
| abstract_inverted_index.features | 148, 154 |
| abstract_inverted_index.learning | 115, 127 |
| abstract_inverted_index.methods. | 183 |
| abstract_inverted_index.negative | 55, 70 |
| abstract_inverted_index.network, | 3 |
| abstract_inverted_index.networks | 46, 165 |
| abstract_inverted_index.positive | 48, 68 |
| abstract_inverted_index.problem. | 41, 109 |
| abstract_inverted_index.properly | 131 |
| abstract_inverted_index.solution | 106 |
| abstract_inverted_index.transfer | 114 |
| abstract_inverted_index.Questions | 13 |
| abstract_inverted_index.anti-fan, | 58 |
| abstract_inverted_index.component | 139 |
| abstract_inverted_index.effective | 105 |
| abstract_inverted_index.framework | 116 |
| abstract_inverted_index.introduce | 103 |
| abstract_inverted_index.networks. | 76 |
| abstract_inverted_index.overtaken | 10 |
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| abstract_inverted_index.algorithms | 128 |
| abstract_inverted_index.framework, | 143 |
| abstract_inverted_index.important. | 23 |
| abstract_inverted_index.prediction | 177 |
| abstract_inverted_index.classifier. | 98 |
| abstract_inverted_index.demonstrate | 170 |
| abstract_inverted_index.effectively | 66 |
| abstract_inverted_index.information | 86 |
| abstract_inverted_index.performance | 189 |
| abstract_inverted_index.topological | 147 |
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| abstract_inverted_index.(opposition, | 57 |
| abstract_inverted_index.Experimental | 161 |
| abstract_inverted_index.classifiers. | 140 |
| abstract_inverted_index.incorporates | 130 |
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| abstract_inverted_index.interactions | 8 |
| abstract_inverted_index.Additionally, | 184 |
| abstract_inverted_index.Specifically, | 61 |
| abstract_inverted_index.boosting-based | 126 |
| abstract_inverted_index.user-generated | 5 |
| abstract_inverted_index.decision-making | 159 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.73579787 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |