Rumor Source Detection in Finite Graphs with Boundary Effects by Message-passing Algorithms

Yu, Pei-Duo; Tan, Chee Wei; Fu, Hung‐Lin

doi:10.1145/3110025.3110028

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…Second, even for regular trees, the available performance guarantee is far from being useful in practice. Even in the most idealized situation of infinite regular trees, the correct probability of the rumor center is almost always below 0.3 (Shah & Zaman, 2011;Dong et al, 2013;Yu et al, 2018). For general graphs, as we show later, the correct rate of such a single-point estimation method only becomes too low to be practical.…”

Section: Introductionmentioning

confidence: 92%

“…Though early practices have been done for this important problem with motivations from various domains, systematic research on this problem only began very recently, arguably starting from the seminal work of Shah & Zaman (2011), which proposed a rumor center estimator that can be located by an efficient message-passing algorithm with linear time complexity. Despite the significant interest and progress on this problem in recent years (Shah & Zaman, 2012;Dong et al, 2013;Khim & Loh, 2016;Bubeck et al, 2017;Yu et al, 2018;Crane & Xu, 2020), many challenges remain unaddressed. First, the theoretical understanding of these methods is currently only available under very restrictive and somewhat unrealistic structural assumptions of the networks such as regular trees.…”

Section: Introductionmentioning

confidence: 99%

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Diffusion Source Identification on Networks with Statistical Confidence

Dawkins,

Li,

2021

Preprint

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Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification. Though this problem has received significant recent attention, most studies have focused only on very restrictive settings and lack theoretical guarantees for more realistic networks. We introduce a statistical framework for the study of diffusion source identification and develop a confidence set inference approach inspired by hypothesis testing. Our method efficiently produces a small subset of nodes, which provably covers the source node with any prespecified confidence level without restrictive assumptions on network structures. Moreover, we propose multiple Monte Carlo strategies for the inference procedure based on network topology and the probabilistic properties that significantly improve the scalability. To our knowledge, this is the first diffusion source identification method with a practically useful theoretical guarantee on general networks. We demonstrate our approach via extensive synthetic experiments on well-known random network models and a mobility network between cities concerning the COVID-19 spreading.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Diffusion Source Identification on Networks with Statistical Confidence

Dawkins,

Li,

2021

Preprint

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“…The identification of sources is critical in different fields of operation Because of its wide variety of uses, major advances in the identification of origins have been observed in the last two decades. Significant research has been conducted into sources in a range of application areas, such as healthcare (the first patient to be discovered to monitor an influenza pandemic) [53], surveillance (computer virus sources) [54], and wide interconnected networks (wireless sensor network gas leak source [55], e-mail network source [56], dynamic network propagating sources [57] and social network rumor disinformation sources [23,27]).…”

Section: Related Studiesmentioning

confidence: 99%

“…Some studies worked with rumor source identification as a tree-like network [19][20][21][22]. Yu et al [23] applied a finite graph and use the message-passing approach for source detection, to reduce the search of vertices for estimating the maximum likelihood. In another approach, Xu et al [24] proposed a source detection method by applying sensor nodes in the network that do not use the rumor's text.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

Qasem¹,

Al-Sarem²,

Saeed³

2022

Computers, Materials &Amp; Continua

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Rumors regarding epidemic diseases such as COVID 19, medicines and treatments, diagnostic methods and public emergencies can have harmful impacts on health and political, social and other aspects of people's lives, especially during emergency situations and health crises. With huge amounts of content being posted to social media every second during these situations, it becomes very difficult to detect fake news (rumors) that poses threats to the stability and sustainability of the healthcare sector. A rumor is defined as a statement for which truthfulness has not been verified. During COVID 19, people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media. Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information. However, very few studies have been conducted for this purpose for the Arabic language, which has unique characteristics. Therefore, this paper proposes a comprehensive approach which includes two phases: detection and tracking. In the detection phase of the study carried out, several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset. A new detection model was used which combined two models: The Genetic Algorithm Based Support Vector Machine (that works on users' and tweets' features) and the stacking ensemble method (that works on tweets' texts). In the tracking phase, several similarity-based techniques were used to obtain the top 1% of similar tweets to a target tweet/post, which helped to find the source of the rumors. The experiments showed interesting results in terms of accuracy, precision, recall and F1-Score for rumor detection (the accuracy reached 92.63%), and showed interesting findings in the tracking phase, in terms of ROUGE L precision, recall and F1-Score for similarity techniques.

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“…Specifically, some methods provide in-depth analysis around content features, e.g., linguistic (Reis et al, 2019), semantic (De Sarkar et al, 2018), emotional (Ajao et al, 2019), and stylistic (Potthast et al, 2018), and achieve limited performance. On this basis, some work additionally extracts various social context features as credibility features, including meta-data based (i.e., source-based (Rathore et al, 2017;Yu et al, 2018), user-centered (Long et al, 2017;Ribeiro et al, 2017), and post-based (Wang, 2017;Ma et al, 2018b)) and networkbased (Ruchansky et al, 2017;Liu & Wu, 2018;, and promotes the development of different fusion approaches, such as hybrid-CNN model (Wang, 2017), CSI model (Ruchansky et al, 2017), and tree-structured RNN (Ma et al, 2018b), which gain remarkable performance boosts compared to other models only capturing text features. From these methods, we can find that expanding features can significantly improve the performance of credibility evaluation.…”

mentioning

confidence: 99%

Discovering differential features: Adversarial learning for information credibility evaluation

Rao

Nazir

et al. 2020

Information Sciences

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A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves the state-of-theart performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twit-ter16 validate the robustness of the model with 1.8% performance improvements.

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Rumor Source Detection in Finite Graphs with Boundary Effects by Message-passing Algorithms

Cited by 12 publications

References 9 publications

Diffusion Source Identification on Networks with Statistical Confidence

Diffusion Source Identification on Networks with Statistical Confidence

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

Discovering differential features: Adversarial learning for information credibility evaluation

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