Towards large-scale social networks with online diffusion provenance detection

Wang, Haishuai; Wu, Jia; Pan, Shirui; Zhang, Peng; Chen, Ling

doi:10.1016/j.comnet.2016.08.025

Cited by 23 publications

(13 citation statements)

References 22 publications

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“…In the future, we will take more side-information (e.g., the social relationships between users and the surrounding text information of images) into consideration in order to further enhance the effectiveness of the click predicting and visual ranking. In addition, inspired by the data stream mining techniques [22,23], another extension of this work is to study the incremental solutions to the tasks of click predicting and graph ranking.…”

Section: Discussionmentioning

confidence: 99%

Click-boosted graph ranking for image retrieval

Qin

et al. 2017

COMSIS J

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Graph ranking is one popular and successful technique for image retrieval, but its effectiveness is often limited by the well-known semantic gap. To bridge this gap, one of the current trends is to leverage the click-through data associated with images to facilitate the graph-based image ranking. However, the sparse and noisy properties of the image click-through data make the exploration of such resource challenging. Towards this end, this paper propose a novel click-boosted graph ranking framework for image retrieval, which consists of two coupled components. Concretely, the first one is a click predictor based on matrix factorization with visual regularization, in order to alleviate the sparseness of the click-through data. The second component is a soft-label graph ranker that conducts the image ranking by using the enriched click-through data noise-tolerantly. Extensive experiments for the tasks of click predicting and image ranking validate the effectiveness of the proposed methods in comparison to several existing approaches.

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Section: Discussionmentioning

confidence: 99%

Click-boosted graph ranking for image retrieval

Qin

et al. 2017

COMSIS J

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“…SER is a highly active research field, with many novel approaches being proposed and investigated over the past decade. With the increase of available data and computational power, deep learning methods are rapidly becoming the predominant approach [17], [17]- [19]. In particular, many recent studies have explored leveraging deep neural networks as feature extractors to learn discriminative representation [20].…”

Section: Related Workmentioning

confidence: 99%

Exploring Deep Spectrum Representations via Attention-Based Recurrent and Convolutional Neural Networks for Speech Emotion Recognition

et al. 2019

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The automatic detection of an emotional state from human speech, which plays a crucial role in the area of human-machine interaction, has consistently been shown to be a difficult task for machine learning algorithms. Previous work on emotion recognition has mostly focused on the extraction of carefully hand-crafted and highly engineered features. Results from these works have demonstrated the importance of discriminative spatio-temporal features to model the continual evolutions of different emotions. Recently, spectrogram representations of emotional speech have achieved competitive performance for automatic speech emotion recognition (SER). How machine learning algorithms learn the effective compositional spatio-temporal dynamics for SER has been a fundamental problem of deep representations, herein denoted as deep spectrum representations. In this paper, we develop a model to alleviate this limitation by leveraging a parallel combination of attention-based bidirectional long short-term memory recurrent neural networks with attention-based fully convolutional networks (FCN). The extensive experiments were undertaken on the interactive emotional dyadic motion capture (IEMOCAP) and FAU aibo emotion corpus (FAU-AEC) to highlight the effectiveness of our approach. The experimental results indicate that deep spectrum representations extracted from the proposed model are well-suited to the task of SER, achieving a WA of 68.1 % and a UA of 67.0 % on IEMOCAP, and 45.4% for UA on FAU-AEC dataset. Key results indicate that the extracted deep representations combined with a linear support vector classifier are comparable in performance with eGeMAPS and COMPARE, two standard acoustic feature representations.

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“…Whenever a test time-variant graph arrives, the algorithm converts it to transformation sequences and computes the distance between the test transformation sequences and features selected for different classes by using Edit Similarity (Definition 5). The output label of the test graph is the same as the label of the graph-shapelet pattern, which has maximum similarity to the test time-variant graphs (lines [12][13][14][15][16][17].…”

Section: E Classification With Graph-shapelet Patternsmentioning

confidence: 99%

“…A sudden increase in occurrences of information propagated over social media in a particular time and place is known as outbreak information propagation [15]. In other words, outbreak occurs when information is propagated to a large audience in a very short time (e.g., a common case of outbreak is rumor or malicious information diffusion [16]). At a specific point in time, the status of the information diffusion is a graph, but over time the graph is diverse.…”

Section: Introductionmentioning

confidence: 99%

Time-Variant Graph Classification

Wang

Zhu

et al. 2018

IEEE Trans. Syst. Man Cybern, Syst.

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Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet -a class of discriminative features designed for vector-based temporal data classification. To discover graphshapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms.

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Towards large-scale social networks with online diffusion provenance detection

Cited by 23 publications

References 22 publications

Click-boosted graph ranking for image retrieval

Click-boosted graph ranking for image retrieval

Exploring Deep Spectrum Representations via Attention-Based Recurrent and Convolutional Neural Networks for Speech Emotion Recognition

Time-Variant Graph Classification

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