A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data

Gao, Sheng; Huacan, Pang; Gallinari, Patrick; Guo, Jun; Kato, Nei

doi:10.1109/tii.2017.2684160

Cited by 63 publications

(42 citation statements)

References 21 publications

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“… Main ideas Advantages Disadvantages Evaluation methods Tools Case studies Graph-based ( Kumaran and Chitrakala, 2017 ) Introducing a social influence rank-based determination method on big data streams in online social networks High scalability High accuracy over time Decreasing running and computation time The fixed size of the sample Additional features could be added in future Data sets Python,Hadoop,MongoDB Twitter ( Persico et al, 2018 ) Introducing an influence maximization and diffusion algorithm High scalability Reduce running time Low cost High accuracy High recall Supporting SN applications Not evaluating the performance of these two architectures on other datasets Low privacy-preserving Real test bed Apache Storm, Apache Spark, Microsoft Azure HDInsight Yahoo Flickr Creative Commons 100 Million (YFCC100M) ( Gao et al, 2017 ) Presenting an information-dependent embedding based diffusion prediction model High precision Efficient diffusion Prediction speed (low response time) Not considering the social structure in the proposed embedding model Real test bed Not mentioned Digg,Meme tracker,GOOGLE + ( Elkin et al, 2017 ) Introducing a network-based model to predict disease activity across geographical locations Being able to predict disease and helping to control diseases High accuracy High F- measure Low scalability Not considering other factors besides geographic locations such as weather patterns Real test bed Not mentioned Twitter ( Wang et ...…”

Section: Classification Of the Selected Papersmentioning

confidence: 99%

“…In general, reports showed that Lambda performed better, and both architectures supported SN applications properly. To predict information diffusion in the content of social big data, Gao, et al ( Gao et al, 2017 ) offered an efficient Information-dependent Embedding Based Diffusion Prediction (IEDP) model. They also extended a typical margin-based optimization algorithm and presented an efficient learning algorithm based on Stochastic Gradient Descent (SGD).…”

Section: Classification Of the Selected Papersmentioning

confidence: 99%

See 1 more Smart Citation

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Abkenar

Kashani

Mahdipour

et al. 2021

Telematics and Informatics

110

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Section: Classification Of the Selected Papersmentioning

confidence: 99%

Section: Classification Of the Selected Papersmentioning

confidence: 99%

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Abkenar

Kashani

Mahdipour

et al. 2021

Telematics and Informatics

110

View full text Add to dashboard Cite

“…In recent years, as more and more people enjoy the services provided by Facebook, Twitter, and Weibo, etc., information cascades have become ubiquitous in online social networks, which has motivated a huge amount of researches [1,2,3,4,5]. An important research topic is information cascade prediction, whose purpose is to predict who will be infected by a piece of information in the future [6,7,8,9], where infection refers to the actions that users reshare (retweet) or comment a tweet, a photo, or other piece of information [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Collaborative Embedding for information cascade prediction

Zhao

Yang

Lin

et al. 2020

Knowledge-Based Systems

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Recently, information cascade prediction has attracted increasing interest from researchers, but it is far from being well solved partly due to the three defects of the existing works. First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion. Second, the existing works often ignore the prediction of the infection order, which also plays an important role in social network analysis. At last, the existing works often depend on the requirement of underlying diffusion networks which are likely unobservable in practice. In this paper, we aim at the prediction of both node infection and infection order without requirement of the knowledge about the underlying diffusion mechanism and the diffusion network, where the challenges are two-fold. The first is what cascading characteristics of nodes should be captured and how to capture them, and the second is that how to model the non-linear features of nodes in information cascades. To address these challenges, we propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction, which can capture not only the node structural property but also two kinds of node cascading characteristics. We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration, in which way the non-linearity of information cascades * Ning Yang and Tao Lin share the corresponding authorship.can be effectively captured. The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.

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“…The underlying idea is that similar nodes in the graph are mapped to close-by vectors in the embedding space. Using these representations, traditional machine learning methods can be applied on network data to perform downstream tasks such as link prediction [11,37], information diffusion [4,9,23], and multi-label classification [35].…”

Section: Introductionmentioning

confidence: 99%

CSNE: Conditional Signed Network Embedding

Mara

Mashayekhi

Lijffijt

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing network embedding methods for sign prediction, however, generally enforce different notions of status or balance theories in their optimization function. These theories, are often inaccurate or incomplete which negatively impacts method performance. In this context, we introduce conditional signed network embedding (CSNE). Our novel probabilistic approach models structural information about the signs in the network separately from finegrained detail. Structural information is represented in the form of a prior, while the embedding itself is used for capturing fine-grained information. These components are then integrated in a rigorous manner. CSNE's accuracy depends on the existence of sufficiently powerful structural priors for modelling signed networks, currently unavailable in the literature. Thus, as a second main contribution, which we find to be highly valuable in its own right, we also introduce a novel approach to construct priors based on the Maximum Entropy (MaxEnt) principle. These priors can model the polarity of nodes (the degree to which their links are positive) as well as signed triangle counts (a measure for the degree structural balance holds to in a network). Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt priors on their own, while less accurate than full CSNE, achieve accuracies competitive with the state-of-the-art at very limited computational cost, thus providing an excellent runtime-accuracy trade-off in resource-constrained situations. CCS CONCEPTS • Computing methodologies → Machine learning; Knowledge representation and reasoning.

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A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data

Cited by 63 publications

References 21 publications

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Deep Collaborative Embedding for information cascade prediction

CSNE: Conditional Signed Network Embedding

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