Graph Mining Meets Fake News Detection

Yu, Kaiqiang; Long, Cheng

doi:10.1007/978-3-030-62696-9_8

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…Detecting fake news is essential for a healthy society, and there are several different approaches to detecting fake news. From a machine learning point of view, fake news detection is one of binary classification cases [4].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine For Hoax Detection

Saraswati,

Putra,

Muku

et al. 2023

SINTECH Journal

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Along with the development of information technology, news media has also developed by presenting information online Along with the rapid development of online news, the spread of fake news information (hoaxes) is also increasing rapidly and widely. Hoax news is often spread intentionally for various purposes. Generally, hoax news aims to direct the reader's perception to believe in a bad perception of an event, character or even a company. The motivation is to invite readers to believe something that is not true with the aim of benefiting the news disseminator is something dangerous. This research aims to detect English-language hoaxes by applying the Support vector machine (SVM) algorithm. In this study, the data used are two data sources, namely English news datasets from Kaggle and English news taken from BBC. The results of this study show that the application of the SVM algorithm turns out to get good performance because the model is able to classify hoax news with an accuracy of 99.4% on Kaggle data while on the BBC news dataset the model gets an accuracy of 98.9%. This research also shows that the SVM method is proven to have good generalization properties. Where it is able to identify test data that is completely different from the training data.

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Section: Introductionmentioning

confidence: 99%

Support Vector Machine For Hoax Detection

Saraswati,

Putra,

Muku

et al. 2023

SINTECH Journal

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“…For a given bipartite graph, a dense/cohesive subgraph within it usually carries interesting information that can be used for solving practical problems such as fraud detection [17,46], online recommendation [18,36] and community search [19,44]. For example, in social networking applications, when a group of users are paid to promote a specific set of fake articles via retweets, the induced subgraph by these users and the articles would be dense.…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for Maximal k-Biplex Enumeration

Yu¹,

Cheng²,

Liu³

et al. 2021

Preprint

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Mining maximal subgraphs with cohesive structures from a bipartite graph has been widely studied. One important cohesive structure on bipartite graphs is 𝑘-biplex, where each vertex on one side disconnects at most 𝑘 vertices on the other side. In this paper, we study the maximal 𝑘-biplex enumeration problem which enumerates all maximal 𝑘-biplexes. Existing methods suffer from efficiency and/or scalability issues and have the time of waiting for the next output exponential w.r.t. the size of the input bipartite graph (i.e., an exponential delay). In this paper, we adopt a reverse search framework called bTraversal, which corresponds to a depth-first search (DFS) procedure on an implicit solution graph on top of all maximal 𝑘-biplexes. We then develop a series of techniques for improving and implementing this framework including (1) carefully selecting an initial solution to start DFS, (2) pruning the vast majority of links from the solution graph of bTraversal, and (3) implementing abstract procedures of the framework. The resulting algorithm is called iTraversal, which has its underlying solution graph significantly sparser than (around 0.1% of) that of bTraversal. Besides, iTraversal provides a guarantee of polynomial delay. Our experimental results on real and synthetic graphs, where the largest one contains one billion edges, show that our algorithm is up to four orders of magnitude faster than existing algorithms. CCS CONCEPTS• Mathematics of computing → Graph algorithms.

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“…The MQCE problem has been widely studied in the past [1,20,[29][30][31][32] and used for various applications such as discovering biologically relevant functional groups [8][9][10][11], finding social communities [2,40], detecting anomaly [5][6][7], etc. For example, authors in [20] conduct a case study that finds biologically relevant functional groups by mining large MQCs which have the size at least a threshold and appear in each graph from a set of protein-protein interaction and gene-gene interaction graphs.…”

Section: Maximal Quasi-clique Enumerationmentioning

confidence: 99%

“…For a given graph, it aims to extract dense/cohesive subgraphs that carry interesting information for solving practical problems such as community search [2], online recommendation [3,4], fraud detection [5][6][7], biologically functional structure discovery [8][9][10][11],…”

Section: Introduction 11 Background and Motivationsmentioning

confidence: 99%

Cohesive subgraph mining in large graphs

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I would like to express my sincere gratitude to my supervisor, Prof. Cheng Long, for his continuous support and patient guidance throughout my Ph.D studies. Specifically, I feel extraordinarily grateful to Cheng for training me the skills of approaching research problems, writing papers and making presentation, providing invaluable feedbacks and suggestions constantly, and encouraging me to overcome the difficulties in my research.Far beyond that, his brilliant insights and intense enthusiasm have greatly shaped my attitude towards research. I am forever thankful for these help from Cheng.

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Graph Mining Meets Fake News Detection

Cited by 4 publications

References 32 publications

Support Vector Machine For Hoax Detection

Support Vector Machine For Hoax Detection

Efficient Algorithms for Maximal k-Biplex Enumeration

Cohesive subgraph mining in large graphs

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