Overlapping Community Detection for Multimedia Social Networks

Huang, Faliang; Li, Xuelong; Zhang, Shichao; Zhang, Jilian; Chen, Jinhui; Zhai, Zhinian

doi:10.1109/tmm.2017.2692650

Cited by 31 publications

(30 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a nutshell, the orchestrator is an ETL 3 pipeline [29] adapted to the structure of the MixedEmotions, thus, it is suited to work with Docker containers deployed in Mesos 4 (Fig. 1), as well as external services as long as they have a REST API 5 . It is fully configurable with plain text configuration files, so a user does not need to have programming skills.…”

Section: Architecture Overviewmentioning

confidence: 99%

See 1 more Smart Citation

MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis

Buitelaar

Wood

Negi

et al. 2018

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Recently, there is an increasing tendency to embed functionalities for recognizing emotions from user generated media content in automated systems such as call-centre operations, recommendations and assistive technologies, providing richer and more informative user and content profiles. However, to date, adding these functionalities was a tedious, costly, and time consuming effort, requiring identification and integration of diverse tools with diverse interfaces as required by the use case at hand. The MixedEmotions Toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: (i) for text processing: emotion and sentiment recognition, (ii) for audio processing: emotion, age, and gender recognition, (iii) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation, and (iv) for linked data: knowledge graph integration. Moreover, the MixedEmotions Toolbox is open-source and free. In this article, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standard test-beds showing its state-of-the-art performance. Furthermore, three real-world use-cases show its effectiveness, namely emotion-driven smart TV, call center monitoring, and brand reputation analysis.

show abstract

Section: Architecture Overviewmentioning

confidence: 99%

“…Extract, Transform, Load 4 mesos.apache.org: The Mesos kernel runs on every machine and provides applications with API's for resource management and scheduling across entire datacenter and cloud environments 5. REST = Representational State Transfer, API=Application Programming Interface.…”

mentioning

confidence: 99%

MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis

Buitelaar

Wood

Negi

et al. 2018

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

“…Analyzing these networks yields insight into their structures. Growing attention has been paid to this analysis in recent years, particularly due to the omnipresence of such networks in real-world [1].…”

Section: Introductionmentioning

confidence: 99%

Detecting Overlapping Communities in Social Networks using Deep Learning

Salehi

Pouyan

2020

IJE

View full text Add to dashboard Cite

In network analysis, the community is considered as a group of nodes that is densely connected with respect to the rest of the network. Detecting the community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There are various approaches in literature for community, overlapping or disjoint, detection in networks. In recent years, many researchers have concentrated on feature learning and network embedding methods for nodes clustering. These methods map the network into a lower-dimensional representation space. In this paper, we propose a model for learning graph representation using deep neural networks. In this method, a nonlinear embedding of the original graph is fed to stacked auto-encoders for learning the model. Then an overlapping clustering algorithm is employed to extract overlapping communities. The effectiveness of the proposed model is investigated by conducting experiments on standard benchmarks and real-world datasets of varying sizes. Empirical results exhibit that the presented method outperforms some popular community detection methods.

show abstract

“…Then, we intend to cluster TSCs into different topics. For the videos with low-density TSCs, we propose a dialogue-based clustering algorithm, which is inspired by community detection theory [12,18,24]. For the videos with high-density TSCs, we propose a topic center-based cluster algorithm, which is a novel hierarchical agglomerative clustering [37,39,41].…”

Section: Introductionmentioning

confidence: 99%

Time-Sync Video Tag Extraction Using Semantic Association Graph

Yang

Wang

Ruan

et al. 2019

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the time-sync comments. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.

show abstract

Overlapping Community Detection for Multimedia Social Networks

Cited by 31 publications

References 44 publications

MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis

MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis

Detecting Overlapping Communities in Social Networks using Deep Learning

Time-Sync Video Tag Extraction Using Semantic Association Graph

Contact Info

Product

Resources

About