Toward Personalized Relational Learning

Li, Jundong; Wu, Liang; Zaı̈ane, Osmar R.; Liu, Huan

doi:10.1137/1.9781611974973.50

Cited by 30 publications

(19 citation statements)

References 33 publications

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“…LINE [34] aims to preserve the rst-and second-order proximity between nodes, and provides an embedding vector by concatenating results on both levels. Our work focuses on encoding both social proximity and social community information to alleviate the data sparsity, instead of investigating only one of them [22,23]. Recent studies also study and utilize network dynamics by observing the change of social networks over time [19,21].…”

Section: Related Workmentioning

confidence: 99%

Tracing Fake-News Footprints

Liu

2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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227

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When a message, such as a piece of news, spreads in social networks, how can we classify it into categories of interests, such as genuine or fake news? Classication of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classication accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.

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Section: Related Workmentioning

confidence: 99%

Tracing Fake-News Footprints

Liu

2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Self Cite

227

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“…The research can be dated back to studies on social dimensions [19], and recent work on network embedding also shows superior accuracy on network clustering and classification [18]. These methods solve the problem through learning a low-rank representation social actors, which can be regarded as extracting features from the network [15,14]. In order to achieve a higher accuracy, previous studies assumed nodes are not equally weighted, and Copyright © 2018 by SIAM Unauthorized reproduction of this article is prohibited RLM ranks data instances with the learned weight in a descending order, RNMF ranks data with the training loss, and we adopt a Random baseline that selects nodes at random.…”

Section: Related Workmentioning

confidence: 99%

Toward Relational Learning with Misinformation

Liu

2018

Proceedings of the 2018 SIAM International Conference on Data Mining

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Relational learning has been proposed to cope with the interdependency among linked instances in a network, and it is a fundamental tool to categorize social network users for various tasks. However, the emerging widespread of misinformation in social networks, information that is inaccurate or false, poses novel challenges to utilizing social media data. Malicious users may actively manipulate their content and characteristics, which easily lead to a noisy dataset. Hence, it is intricate for traditional relational learning approaches to deliver an accurate predictive model in the presence of misinformation. In this work, we precisely focus on the problem by proposing a joint framework that simultaneously constructs a relational learning model and mitigates the effect of misinformation by restraining anomalous points. Empirical results on real-world social media data prove the superiority of the proposed approach, Relational Learning with Misinformation (RLM), over traditional approaches on modeling social network users.

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“…e root cause of the correlations can be a ributed to social influence and homophily effect in social science theories [30,31]. Also, many real-world applications, such as node classification, community detection, topic modeling and anomaly detection [18,22,24,28,51], have shown significant improvements by modeling such correlations.…”

Section: Introductionmentioning

confidence: 99%

Attributed Network Embedding for Learning in a Dynamic Environment

Dani

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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326

188

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Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation. e learned embeddings could advance various learning tasks such as node classi cation, network clustering, and link prediction. Most, if not all, of the existing work, is overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure o en evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node a ributes, and their a ribute values are also naturally changing, with the emerging of new content and the fading of old content. ese changing characteristics motivate us to seek an e ective embedding representation to capture network and a ribute evolving pa erns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the rst to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic a ributed network embedding framework -DANE. In particular, DANE provides an o ine method for a consensus embedding rst and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real a ributed networks to corroborate the e ectiveness and e ciency of the proposed framework.

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Toward Personalized Relational Learning

Cited by 30 publications

References 33 publications

Tracing Fake-News Footprints

Tracing Fake-News Footprints

Toward Relational Learning with Misinformation

Attributed Network Embedding for Learning in a Dynamic Environment

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