Using Hierarchies in Online Social Networks to Determine Link Prediction

Ahuja, Ravinder; Singhal, Vipul; Banga, Alisha

doi:10.1007/978-981-13-3393-4_8

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…In recent years, OSM grew into one of the most popular communication technologies for various types of personal relationships [1,2]. Most people expose their opinions, talk to loved ones, and share professional information and news about the world [1,3].…”

Section: Introductionmentioning

confidence: 99%

Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

et al. 2020

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Online Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure. The majority of studies on automatic fake news detection are restricted to English documents, with few works evaluating other languages, and none comparing language-independent characteristics. Moreover, the spreading of deceptive news tends to be a worldwide problem; therefore, this work evaluates textual features that are not tied to a specific language when describing textual data for detecting news. Corpora of news written in American English, Brazilian Portuguese, and Spanish were explored to study complexity, stylometric, and psychological text features. The extracted features support the detection of fake, legitimate, and satirical news. We compared four machine learning algorithms (k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) to induce the detection model. Results show our proposed language-independent features are successful in describing fake, satirical, and legitimate news across three different languages, with an average detection accuracy of 85.3% with RF.

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Section: Introductionmentioning

confidence: 99%

Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

et al. 2020

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“…Most of the study of link prediction has been done in the last few years as a result of its use in several disciplines, namely, complex evolving online social networks (Almgren and Lee, 2016; Ahuja et al , 2019), making suggestions for peers on social media (Ma et al , 2016; Shabaz and Garg, 2021), chemioinformatics of three-dimensional chemical molecule structure ecological systems of species (Nikolentzos et al , 2021), discover hidden relationships in a field of security (Kumar et al , 2020; Daud et al , 2020), citation networks (Zhou et al , 2018) and social relationships of users in personalized recommender systems (Ebrahimi and Golpayegani, 2016).…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

Bhattacharya

Nagwani

Tripathi

2022

DTA

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PurposeSocial networking platforms are increasingly using the Follower Link Prediction tool in an effort to expand the number of their users. It facilitates the discovery of previously unidentified individuals and can be employed to determine the relationships among the nodes in a social network. On the other hand, social site firms use follower–followee link prediction (FFLP) to increase their user base. FFLP can help identify unfamiliar people and determine node-to-node links in a social network. Choosing the appropriate person to follow becomes crucial as the number of users increases. A hybrid model employing the Ensemble Learning algorithm for FFLP (HMELA) is proposed to advise the formation of new follower links in large networks.Design/methodology/approachHMELA includes fundamental classification techniques for treating link prediction as a binary classification problem. The data sets are represented using a variety of machine-learning-friendly hybrid graph features. The HMELA is evaluated using six real-world social network data sets.FindingsThe first set of experiments used exploratory data analysis on a di-graph to produce a balanced matrix. The second set of experiments compared the benchmark and hybrid features on data sets. This was followed by using benchmark classifiers and ensemble learning methods. The experiments show that the proposed (HMELA) method predicts missing links better than other methods.Practical implicationsA hybrid suggested model for link prediction is proposed in this paper. The suggested HMELA model makes use of AUC scores to predict new future links. The proposed approach facilitates comprehension and insight into the domain of link prediction. This work is almost entirely aimed at academics, practitioners, and those involved in the field of social networks, etc. Also, the model is quite effective in the field of product recommendation and in recommending a new friend and user on social networks.Originality/valueThe outcome on six benchmark data sets revealed that when the HMELA strategy had been applied to all of the selected data sets, the area under the curve (AUC) scores were greater than when individual techniques were applied to the same data sets. Using the HMELA technique, the maximum AUC score in the Facebook data set has been increased by 10.3 per cent from 0.8449 to 0.9479. There has also been an 8.53 per cent increase in the accuracy of the Net Science, Karate Club and USAir databases. As a result, the HMELA strategy outperforms every other strategy tested in the study.

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“…Online social networks [1], biological networks, such as protein-protein interactions and genetic interactions between organisms [2], ecological systems of species, knowledge graphs [3], citation networks [4], and social relationships of users in personalized recommender systems [5], are all instances of graphs of complex interactions, which are also referred to as complex networks. While these networks are almost always dynamic in nature, a vital query is how they change over time.…”

Section: Introductionmentioning

confidence: 99%

Review on Learning and Extracting Graph Features for Link Prediction

Mutlu

Oghaz

Rajabi

et al. 2020

MAKE

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Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.

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Using Hierarchies in Online Social Networks to Determine Link Prediction

Cited by 6 publications

References 9 publications

Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

Review on Learning and Extracting Graph Features for Link Prediction

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