Journal articles: 'Graph, social and multimedia data'

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Wagenpfeil, Stefan, Binh Vu, Paul Mc Kevitt, and Matthias Hemmje. "Fast and Effective Retrieval for Large Multimedia Collections." Big Data and Cognitive Computing 5, no. 3 (July 22, 2021): 33. http://dx.doi.org/10.3390/bdcc5030033.

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The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the Graph Code, which also enhances the ranked result representations. Thus, Graph Codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information.

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Xu, Zheng, Zhiguo Yan, Yunhuai Liu, and Lin Mei. "Measuring the Semantic Relatedness Between Images Using Social Tags." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 2 (April 2013): 1–12. http://dx.doi.org/10.4018/ijcini.2013040101.

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Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, the authors aim at measuring the semantic relatedness of Flickr images. Firstly, information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robust of the proposed method.

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Xu, Zheng, Xiangfeng Luo, Yunhuai Liu, Lin Mei, and Chuanping Hu. "Measuring Semantic Relatedness between Flickr Images: From a Social Tag Based View." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/758089.

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Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, we aim at measuring the semantic relatedness of Flickr images. Firstly, four information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. Thirdly, the order information of tags is added to measure the semantic relatedness, which emphasizes the tags with high positions. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robustness of the proposed method. Moreover, some applications such as searching and faceted exploration are introduced using the proposed method, which shows that the proposed method has broad prospects on web based tasks.

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Mozhaiev, Mykhailo, and Pavlo Buslov. "METHOD OF MODELING OF A SOCIAL PROFILE USING BIG DATA STRUCTURE TRANSFORMATION OPTIMIZATION." Advanced Information Systems 5, no. 1 (June 22, 2021): 12–17. http://dx.doi.org/10.20998/2522-9052.2021.1.02.

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The object of the research are methods and algorithms of optimizing of the Big Data transformation to build a social profile model, the subject of the research are methods of constructing of a social profile. For decision-making person, the problem of scientific methodological and instrumental re-equipment is relevant for the effective fulfillment of a set of managerial tasks and confronting of fundamentally new challenges and threats in society. This task is directly related to the problem of building of a model of the social profile of both the individual and the social group as a whole. Therefore, the problem of optimizing of methods of constructing of a mathematical model of a social profile is certainly relevant. During the research, methods of the mathematical apparatus of graph theory, database theory and the concept of non-relational data stores, Big Data technology, text analytics technologies, parallel data processing methods, methods of neural networks' using, methods of multimedia data analyzing were used. These methods were integrated into the general method, called the method of increasing of the efficiency of constructing of a mathematical model of a social profile. The proposed method improves the adequacy of the social profile model, which will significantly improve and simplify the functioning of information systems for decision-making based on knowledge of the social advantages of certain social groups, which will allow dynamic correction of their behavior. The obtained results of testing the method make it possible to consider it as an effective tool for obtaining of an objective information model of a social portrait of a social group. This is because the correctness of setting and solving of the problem ensured that adequate results were obtained. Unlike the existing ones, the proposed modeling method, which uses an oriented graph, allows to improve significantly the quality and adequacy of this process. Further research should be directed towards the implementation of proposed theoretical developments in real decision-making systems. This will increase the weight of automated decision-making systems for social climate analysis.

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Liu, Yan, and Shuo Zhu. "Multimodal Wireless Situational Awareness-Based Tourism Service Scene." Journal of Sensors 2021 (December 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/5503333.

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Community platforms featuring user sharing and self-expression in social media generate big data on tourism resources, which, if fully utilized in a smart tourism system driven by high-tech and new technologies, will bring new life to the field of smart tourism research and will play an important role in the development of Internet+ tourism. However, tourism data in social media has the following characteristics: diversity, redundancy, heterogeneity, and intelligence. To address the characteristics of tourism data in social media, this thesis focuses on the following challenges: it is difficult to efficiently obtain tourism visualization information (text and images) in social media; it is difficult to effectively utilize tourism multimodal heterogeneous information; it is difficult to properly retrieve multimedia entity information of tourism attractions; and it is difficult to reasonably construct tourism personalized recommendation models. In this paper, an image search reordering method based on a hybrid feature graph model is proposed to realize the rapid acquisition of high-quality Internet images from the web using hybrid visual features and graph models, thus providing data security for the analysis of social media-based tourism images. To address the shortcomings of current search engines for image retrieval, visual information is used to bridge the problem of semantic gap between text-based search and images. To address the limitation of single visual features, we use latent semantic analysis to fuse multiple visual features to obtain hybrid features, which not only combine multiple single features but also preserve the potential relationship between these features. To address the shortcomings of the reordering methods based on classification and clustering, a reordering framework based on the graph model is used to reorder the images and finally complete the image search reordering based on the hybrid feature graph model. This method can obtain image information in social media with high efficiency and quality and then prepare for the subsequent work of tourism image analysis mining and personalized recommendation.

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Sakurai, Keigo, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Controllable Music Playlist Generation Based on Knowledge Graph and Reinforcement Learning." Sensors 22, no. 10 (May 13, 2022): 3722. http://dx.doi.org/10.3390/s22103722.

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In this study, we propose a novel music playlist generation method based on a knowledge graph and reinforcement learning. The development of music streaming platforms has transformed the social dynamics of music consumption and paved a new way of accessing and listening to music. The playlist generation is one of the most important multimedia techniques, which aims to recommend music tracks by sensing the vast amount of musical data and the users’ listening histories from music streaming services. Conventional playlist generation methods have difficulty capturing the target users’ long-term preferences. To overcome the difficulty, we use a reinforcement learning algorithm that can consider the target users’ long-term preferences. Furthermore, we introduce the following two new ideas: using the informative knowledge graph data to promote efficient optimization through reinforcement learning, and setting the flexible reward function that target users can design the parameters of itself to guide target users to new types of music tracks. We confirm the effectiveness of the proposed method by verifying the prediction performance based on listening history and the guidance performance to music tracks that can satisfy the target user’s unique preference.

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Zhang, Mingliang, Xiangyang Luo, Pei Zhang, Hao Li, Yi Zhang, and Lingling Li. "High-Capacity Robust Behavioral Steganography Method Based on Timestamp Modulation across Social Internet of Things." Security and Communication Networks 2021 (December 31, 2021): 1–16. http://dx.doi.org/10.1155/2021/6351144.

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Social Internet of Things (SIoT) is an emerging field that combines IoT and Internet, which can provide many novel and convenient application scenarios but still faces challenges in data privacy protection. In this paper, we propose a robust behavioral steganography method with high embedding capacity across social networks based on timestamp modulation. Firstly, the IoT devices on the sending end modulate the secret message to be embedded into a timestamp by using the common property on social networks. Secondly, the accounts of multiple social networks are used as the vertices, and the timestamp mapping relationship generated by the interaction behaviors between them is used as the edges to construct a directed secret message graph across social networks. Then, the frequency of interaction behaviors generated by users of mainstream social networks is analyzed; the corresponding timestamps and social networks are used to implement interaction behaviors based on the secret message graph and the frequency of interaction behaviors. Next, we analyze the frequency of interaction behaviors generated by users in mainstream social networks, implement the interaction behaviors according to the secret message graph and the frequency of interaction behaviors in the corresponding timestamps and social networks, and combine the redundant mapping control to complete the embedding of secret message. Finally, the receiver constructs the timestamp mapping relationship through the shared account, key, and other parameters to achieve the extraction of secret message. The algorithm is robust and does not have the problem that existing multimedia-based steganography methods are difficult to extract the embedded messages completely. Compared with existing graph theory-based social network steganography methods, using timestamps and behaviors frequencies to hide message in multiple social networks increases the cost of detecting covert communication and improves concealment of steganography. At the same time, the algorithm uses a directed secret message graph to increase the number of bits carried by each behavior and improves the embedding capacity. A large number of tests have been conducted on mainstream social networks such as Facebook, Twitter, and Weibo. The results show that the proposed method successfully distributes secret message to multiple social networks and achieves complete extraction of embedded message at the receiving end. The embedding capacity is increased by 1.98–4.89 times compared with the existing methods SSN, NGTASS, and SGSIR.

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Dabhade, Kiran Bhimrao, and C. M. Mankar. "An Optimization of Adaptive Computing-plus-Communication for Multimedia Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 623–28. http://dx.doi.org/10.22214/ijraset.2022.47294.

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Abstract: Cloud data centers become more and more powerful, energy consumption becomes a major challenge both for environmental concerns and for economic reasons, Towards this aim, we present model of social network website and will optimize server in such way that old data server should run on minimum cost. Also this implementation finds out the pattern of data consuming of users and according to that the graphs get generated. And after generating user data consumption data patterns optimize server in such way that whichever server data have less traffic should stay on sleep mode.

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Agosti, Maristella, Maurizio Atzori, Paolo Ciaccia, and Letizia Tanca. "Report on SEBD 2020." ACM SIGIR Forum 54, no. 2 (December 2020): 1–5. http://dx.doi.org/10.1145/3483382.3483392.

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This paper reports on the 28th Italian Symposium on Advanced Database Systems (SEBD 2020), held online as a virtual conference from the 21st to the 24th of June 2020. The topics that were addressed in this edition of the conference were organized in the sessions: ontologies and data integration, anomaly detection and dependencies, text analysis and search, deep learning, noSQL data, trajectories and diffusion, health and medicine, context and ranking, social and knowledge graphs, multimedia content analysis, security issues, and data mining.

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Hou, Li, Qi Liu, Mueen Uddin, Hizbullah Khattak, and Muhammad Asshad. "Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data." Mobile Information Systems 2021 (September 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/8396771.

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Mobile applications are really important nowadays due to providing the accurate check-in data for research. The primary goal of the study is to look into the impact of several forms of entertainment activities on the density dispersal of occupants in Shanghai, China, as well as prototypical check-in data from a location-based social network using a combination of temporal, spatial, and visualization techniques and categories of visitors’ check-ins. This article explores Weibo for big data assessment and its reliability in a variety of categories rather than physically obtained information by examining the link between time, frequency, place, class, and place of check-in based on geographic attributes and related implications. The data for this study came from Weibo, a popular Chinese microblog. It was preprocessed to extract the most important and associated results elements, then converted to geographical information systems format, appraised, and finally displayed using graphs, tables, and heat maps. For data significance, a linear regression model was used, and, for spatial analysis, kernel density estimation was utilized. As per results of hours-to-day usage patterns, enjoyment activities and frequency distribution are produced. Our findings are based on the check-in behaviour of users at amusement locations, the density of check-ins, rush periods for visiting amusement locations, and gender differences. Our data provide light on different elements of human behaviour patterns, the importance of entertainment venues, and their impact in Shanghai. So it can be used in pattern recognition, endorsement structures, and additional multimedia content for these collections.

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Petkos, Georgios, Manos Schinas, Symeon Papadopoulos, and Yiannis Kompatsiaris. "Graph-based multimodal clustering for social multimedia." Multimedia Tools and Applications 76, no. 6 (March 18, 2016): 7897–919. http://dx.doi.org/10.1007/s11042-016-3378-2.

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Zhou, Wei, Zhaoxuan Gong, Wei Guo, Nan Han, and Shaojie Qiao. "Robust Graph Structure Learning for Multimedia Data Analysis." Wireless Communications and Mobile Computing 2021 (June 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/9458188.

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With the rapid development of computer network technology, we can acquire a large amount of multimedia data, and it becomes a very important task to analyze these data. Since graph construction or graph learning is a powerful tool for multimedia data analysis, many graph-based subspace learning and clustering approaches have been proposed. Among the existing graph learning algorithms, the sample reconstruction-based approaches have gone the mainstream. Nevertheless, these approaches not only ignore the local and global structure information but also are sensitive to noise. To address these limitations, this paper proposes a graph learning framework, termed Robust Graph Structure Learning (RGSL). Different from the existing graph learning approaches, our approach adopts the self-expressiveness of samples to capture the global structure, meanwhile utilizing data locality to depict the local structure. Specially, in order to improve the robustness of our approach against noise, we introduce l 2 , 1 -norm regularization criterion and nonnegative constraint into the graph construction process. Furthermore, an iterative updating optimization algorithm is designed to solve the objective function. A large number of subspace learning and clustering experiments are carried out to verify the effectiveness of the proposed approach.

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Wagenpfeil, Stefan, Felix Engel, Paul Mc Kevitt, and Matthias Hemmje. "AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones." Information 12, no. 1 (January 19, 2021): 43. http://dx.doi.org/10.3390/info12010043.

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To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.

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Wagenpfeil, Stefan, Paul Mc Kevitt, Abbas Cheddad, and Matthias Hemmje. "Explainable Multimedia Feature Fusion for Medical Applications." Journal of Imaging 8, no. 4 (April 8, 2022): 104. http://dx.doi.org/10.3390/jimaging8040104.

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Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-rays, and multimedia, the management of a patient’s data has become a huge challenge. In particular, the extraction of features from various different formats and their representation in a homogeneous way are areas of interest in medical applications. Multimedia Information Retrieval (MMIR) frameworks, like the Generic Multimedia Analysis Framework (GMAF), can contribute to solving this problem, when adapted to special requirements and modalities of medical applications. In this paper, we demonstrate how typical multimedia processing techniques can be extended and adapted to medical applications and how these applications benefit from employing a Multimedia Feature Graph (MMFG) and specialized, efficient indexing structures in the form of Graph Codes. These Graph Codes are transformed to feature relevant Graph Codes by employing a modified Term Frequency Inverse Document Frequency (TFIDF) algorithm, which further supports value ranges and Boolean operations required in the medical context. On this basis, various metrics for the calculation of similarity, recommendations, and automated inferencing and reasoning can be applied supporting the field of diagnostics. Finally, the presentation of these new facilities in the form of explainability is introduced and demonstrated. Thus, in this paper, we show how Graph Codes contribute new querying options for diagnosis and how Explainable Graph Codes can help to readily understand medical multimedia formats.

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Zi, Lingling, Junping Du, and Qian Wang. "Domain-Oriented Subject Aware Model for Multimedia Data Retrieval." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/429696.

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With the increment of the scale of internet information as well as the cross-correlation interaction, how to achieve accurate retrieval of multimedia data is an urgent question in terms of efficiently utilizing information resources. However, existing information retrieval approaches provide only limited capabilities to search multimedia data. In order to improve the ability of information retrieval, we propose a domain-oriented subject aware model by introducing three innovative improvements. Firstly, we propose the text-image feature mapping method based on the transfer learning to extract image semantics. Then we put forward the annotation document method to accomplish simultaneous retrieval of multimedia data. Lastly, we present subject aware graph to quantify the semantics of query requirements, which can customize query threshold to retrieve multimedia data. Conducted experiments show that our model obtained encouraging performance results.

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Braun, Peter, Alfredo Cuzzocrea, Carson K. Leung, Adam G. M. Pazdor, and Kimberly Tran. "Knowledge Discovery from Social Graph Data." Procedia Computer Science 96 (2016): 682–91. http://dx.doi.org/10.1016/j.procs.2016.08.250.

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Naaman, Mor. "Social multimedia: highlighting opportunities for search and mining of multimedia data in social media applications." Multimedia Tools and Applications 56, no. 1 (May 21, 2010): 9–34. http://dx.doi.org/10.1007/s11042-010-0538-7.

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Yu, Chen, Yiwen Zhong, Thomas Smith, Ikhyun Park, and Weixia Huang. "Visual Data Mining of Multimedia Data for Social and Behavioral Studies." Information Visualization 8, no. 1 (January 2009): 56–70. http://dx.doi.org/10.1057/ivs.2008.32.

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With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.

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Wattenhofer, Mirjam, Roger Wattenhofer, and Zack Zhu. "The YouTube Social Network." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (August 3, 2021): 354–61. http://dx.doi.org/10.1609/icwsm.v6i1.14243.

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Today, YouTube is the largest user-driven video content provider in the world; it has become a major platform for disseminating multimedia information. A major contribution to its success comes from the user-to-user social experience that differentiates it from traditional content broadcasters. This work examines the social network aspect of YouTube by measuring the full-scale YouTube subscription graph, comment graph, and video content corpus. We find YouTube to deviate significantly from network characteristics that mark traditional online social networks, such as homophily, reciprocative linking, and assortativity. However, comparing to reported characteristics of another content-driven online social network, Twitter, YouTube is remarkably similar. Examining the social and content facets of user popularity, we find a stronger correlation between a user's social popularity and his/her most popular content as opposed to typical content popularity. Finally, we demonstrate an application of our measurements for classifying YouTube Partners, who are selected users that share YouTube's advertisement revenue. Results are motivating despite the highly imbalanced nature of the classification problem.

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Sperlì, Giancarlo, Flora Amato, Vincenzo Moscato, and Antonio Picariello. "Multimedia Social Network Modeling using Hypergraphs." International Journal of Multimedia Data Engineering and Management 7, no. 3 (July 2016): 53–77. http://dx.doi.org/10.4018/ijmdem.2016070104.

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In this paper the authors define a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and to represent in a simple way all the different kinds of relationships that are typical of social networks and multimedia sharing systems, and in particular between multimedia contents, among users and multimedia content and among users themselves. Different applications (e.g. influence analysis, multimedia recommendation) can be then built on the top of the introduce data model thanks to the introduction of proper user and multimedia ranking functions. In addition, the authors provide a strategy for hypergraph learning from social data. Some preliminary experiments concerning efficiency and effectiveness of the proposed approach for analysis of Last.fm network are reported and discussed.

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Lee, Manjai, and Byung-Won On. "Social graph visualization techniques for public data." Journal of the HCI Society of Korea 10, no. 1 (May 31, 2015): 5. http://dx.doi.org/10.17210/jhsk.2015.05.10.1.5.

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Giatsoglou, Maria, and Athena Vakali. "Capturing Social Data Evolution Using Graph Clustering." IEEE Internet Computing 17, no. 1 (January 2013): 74–79. http://dx.doi.org/10.1109/mic.2012.141.

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Zhu, Junnan, Lu Xiang, Yu Zhou, Jiajun Zhang, and Chengqing Zong. "Graph-based Multimodal Ranking Models for Multimodal Summarization." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (May 26, 2021): 1–21. http://dx.doi.org/10.1145/3445794.

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Multimodal summarization aims to extract the most important information from the multimedia input. It is becoming increasingly popular due to the rapid growth of multimedia data in recent years. There are various researches focusing on different multimodal summarization tasks. However, the existing methods can only generate single-modal output or multimodal output. In addition, most of them need a lot of annotated samples for training, which makes it difficult to be generalized to other tasks or domains. Motivated by this, we propose a unified framework for multimodal summarization that can cover both single-modal output summarization and multimodal output summarization. In our framework, we consider three different scenarios and propose the respective unsupervised graph-based multimodal summarization models without the requirement of any manually annotated document-summary pairs for training: (1) generic multimodal ranking, (2) modal-dominated multimodal ranking, and (3) non-redundant text-image multimodal ranking. Furthermore, an image-text similarity estimation model is introduced to measure the semantic similarity between image and text. Experiments show that our proposed models outperform the single-modal summarization methods on both automatic and human evaluation metrics. Besides, our models can also improve the single-modal summarization with the guidance of the multimedia information. This study can be applied as the benchmark for further study on multimodal summarization task.

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Selvan, Mercy Paul, Akansha Gupta, and Anisha Mukherjee. "Give Attention to Overlapping Network Detection in Networks for Multimedia." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3173–77. http://dx.doi.org/10.1166/jctn.2019.8155.

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Finding overlapping agencies from multimedia social networks is an thrilling and important trouble in records mining and recommender systems but, existing overlapping network discovery often generates overlapping community structures with superfluous small groups. Network detection in a multimedia and social network is a conducive difficulty in the network gadget and it helps to understand and learn the overall network shape in element. Those are essentially the dividing wall of network nodes into a few subgroups in which nodes within these subgroups are densely linked, but the connections are sparser in between the subgroups. Social network analysis is widely widespread domain which draws the attention of many information mining experts. Some wide variety of actual community common characteristics which it shares are facebook, Twitter show off the idea of network shape inside the community. Social network is represented as a community graph. Detecting the groups entails locating the densely linked nodes.

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Lin, Po-Chuan, Bo-Wei Chen, and Hangbae Chang. "Concept indexing and expansion for social multimedia websites based on semantic processing and graph analysis." New Review of Hypermedia and Multimedia 22, no. 3 (April 27, 2016): 257–69. http://dx.doi.org/10.1080/13614568.2016.1152318.

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Zhao, Zhanfang, SungKook Han, and JuRi Kim. "R2LD: Schema-based Graph Mapping of relational databases to Linked Open Data for multimedia resources data." Multimedia Tools and Applications 78, no. 20 (February 7, 2019): 28835–51. http://dx.doi.org/10.1007/s11042-019-7281-5.

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Yang, Qing, Tigang Jiang, Wenjia Li, Guangchi Liu, Danda B. Rawat, and Jun Wu. "Editorial: Multimedia and Social Data Processing in Vehicular Networks." Mobile Networks and Applications 25, no. 2 (December 14, 2019): 620–22. http://dx.doi.org/10.1007/s11036-019-01432-2.

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Lin, Sin Hong, and Ming Hong Liao. "Towards publishing social network data with graph anonymization." Journal of Intelligent & Fuzzy Systems 30, no. 1 (September 7, 2015): 333–45. http://dx.doi.org/10.3233/ifs-151759.

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Bhagat, Smriti, Graham Cormode, Balachander Krishnamurthy, and Divesh Srivastava. "Class-based graph anonymization for social network data." Proceedings of the VLDB Endowment 2, no. 1 (August 2009): 766–77. http://dx.doi.org/10.14778/1687627.1687714.

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Gao, Jianliang, Qing Ping, and Jianxin Wang. "Resisting re-identification mining on social graph data." World Wide Web 21, no. 6 (January 18, 2018): 1759–71. http://dx.doi.org/10.1007/s11280-017-0524-3.

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Wang, Shaoting, Guigang Zhang, Phillip Sheu, Masahiro Hayakawa, Hiroyuki Shigematsu, and Atsushi Kitazawa. "Lossy Graph Data Reduction." International Journal of Semantic Computing 12, no. 03 (September 2018): 425–56. http://dx.doi.org/10.1142/s1793351x18500022.

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Graphs are widely used nowadays to store complex data of large applications such as social networks, recommendation engines, computer networks, bio-informatics, just to name a few. Graph data reduction plays a very important role in order to store and process such data efficiently. Many studies about graph data reduction have been done, but most of them are focused on lossless reduction in the sense that query results are preserved after reduction. In this paper, we elaborate on the concept of “lossy” graph reduction for applications that may tolerate approximate results with small but bounded errors in exchange for further data reduction. We study one well known graph problem that is the shortest path problem and design the lossy graph reduction algorithms. The error bounds of the algorithms we propose are proved theoretically. In addition, we implement some of the algorithms based on real world data sets to experimentally investigate the impacts of the error tolerance on the reduction ratio.

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Garg, Muskan, and Mukesh Kumar. "Review on event detection techniques in social multimedia." Online Information Review 40, no. 3 (June 13, 2016): 347–61. http://dx.doi.org/10.1108/oir-08-2015-0281.

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Purpose – Social Media is one of the largest platforms to voluntarily communicate thoughts. With increase in multimedia data on social networking websites, information about human behaviour is increasing. This user-generated data are present on the internet in different modalities including text, images, audio, video, gesture, etc. The purpose of this paper is to consider multiple variables for event detection and analysis including weather data, temporal data, geo-location data, traffic data, weekday’s data, etc. Design/methodology/approach – In this paper, evolution of different approaches have been studied and explored for multivariate event analysis of uncertain social media data. Findings – Based on burst of outbreak information from social media including natural disasters, contagious disease spread, etc. can be controlled. This can be path breaking input for instant emergency management resources. This has received much attention from academic researchers and practitioners to study the latent patterns for event detection from social media signals. Originality/value – This paper provides useful insights into existing methodologies and recommendations for future attempts in this area of research. An overview of architecture of event analysis and statistical approaches are used to determine the events in social media which need attention.

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Atiqah Sia Abdullah, Nur, and Hamizah Binti Anuar. "Review of Data Visualization for Social Media Postings." International Journal of Engineering & Technology 7, no. 4.38 (December 3, 2018): 939. http://dx.doi.org/10.14419/ijet.v7i4.38.27613.

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Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.

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Sang, Jitao, Yue Gao, Bing-kun Bao, Cees Snoek, and Qionghai Dai. "Recent advances in social multimedia big data mining and applications." Multimedia Systems 22, no. 1 (September 28, 2015): 1–3. http://dx.doi.org/10.1007/s00530-015-0482-5.

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Kim, Sul-Ho, Kwon-Jae An, Seok-Woo Jang, and Gye-Young Kim. "Texture feature-based text region segmentation in social multimedia data." Multimedia Tools and Applications 75, no. 20 (January 27, 2016): 12815–29. http://dx.doi.org/10.1007/s11042-015-3237-6.

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Küçükkeçeci, Cihan, and Adnan Yazıcı. "Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks." Big Data Research 11 (March 2018): 33–43. http://dx.doi.org/10.1016/j.bdr.2017.09.003.

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Hong, Hyun-Ki, Gun-Woo Kim, and Dong-Ho Lee. "Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia." Journal of Information Science 44, no. 3 (February 1, 2017): 298–313. http://dx.doi.org/10.1177/0165551517693497.

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The volumes of multimedia content and users have increased on social multimedia sites due to the prevalence of smart mobile devices and digital cameras. It is common for users to take pictures and upload them to image-sharing websites using their smartphones. However, the tag characteristics deteriorate the quality of tag-based image retrieval and decrease the reliability of social multimedia sites. In this article, we propose a semantic tag recommendation technique exploiting associated words that are semantically similar or related to each other using the interwiki links of Wikipedia. First, we generate a word relationship graph after extracting meaningful words from each article in Wikipedia. The candidate words are then rearranged according to importance by applying a link-based ranking algorithm and then the top-k words are defined as the associated words for the article. When a user uploads an image, we collect visually similar images from a social image database. After propagating the proper tags from the collected images, we recommend associated words related to the candidate tags. Our experimental results show that the proposed method can improve the accuracy by up to 14% compared with other works and that exploiting associated words makes it possible to perform semantic tag recommendation.

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Huang, Zhao, and Liu Yuan. "Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies." Discrete Dynamics in Nature and Society 2021 (July 30, 2021): 1–18. http://dx.doi.org/10.1155/2021/2857611.

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People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

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Khan, Abdur Rehman, Umer Rashid, Khalid Saleem, and Adeel Ahmed. "An architecture for non-linear discovery of aggregated multimedia document web search results." PeerJ Computer Science 7 (April 21, 2021): e449. http://dx.doi.org/10.7717/peerj-cs.449.

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The recent proliferation of multimedia information on the web enhances user information need from simple textual lookup to multi-modal exploration activities. The current search engines act as major gateways to access the immense amount of multimedia data. However, access to the multimedia content is provided by aggregating disjoint multimedia search verticals. The aggregation of the multimedia search results cannot consider relationships in them and are partially blended. Additionally, the search results’ presentation is via linear lists, which cannot support the users’ non-linear navigation patterns to explore the multimedia search results. Contrarily, users’ are demanding more services from search engines. It includes adequate access to navigate, explore, and discover multimedia information. Our discovery approach allow users to explore and discover multimedia information by semantically aggregating disjoint verticals using sentence embeddings and transforming snippets into conceptually similar multimedia document groups. The proposed aggregation approach retains the relationship in the retrieved multimedia search results. A non-linear graph is instantiated to augment the users’ non-linear information navigation and exploration patterns, which leads to discovering new and interesting search results at various aggregated granularity levels. Our method’s empirical evaluation results achieve 99% accuracy in the aggregation of disjoint search results at different aggregated search granularity levels. Our approach provides a standard baseline for the exploration of multimedia aggregation search results.

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Sharma, Chirag, Amandeep Bagga, Bhupesh Kumar Singh, and Mohammad Shabaz. "A Novel Optimized Graph-Based Transform Watermarking Technique to Address Security Issues in Real-Time Application." Mathematical Problems in Engineering 2021 (April 8, 2021): 1–27. http://dx.doi.org/10.1155/2021/5580098.

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The multimedia technologies are gaining a lot of popularity these days. Many unauthorized persons are gaining the access of multimedia such as videos, audios, and images. The transmission of multimedia across the Internet by unauthorized person has led to the problem of illegal distribution. The problem arises when copyrighted data is getting accessed without the knowledge of copyright owner. The videos are the most attacked data during COVID-19 pandemic. In this paper, the frame selection video watermarking technique is proposed to tackle the issue. The proposed work enlightens frame selection followed by watermarking embedding and testing of the technique against various attacks. The embedding of the watermark is done on selected frames of the video. The additional security feature Hyperchaotic Encryption is applied on watermark before embedding. Watermark embedding is done using graph-based transform and singular-valued decomposition and the performance of the technique is further optimized using hybrid combination of grey wolf optimization and genetic algorithm. Many researchers face the challenge of quality loss after embedding of watermark. Proposed technique will aim to overcome those challenges. A total of 6 videos (Akiyo, Coastguard, Foreman, News, Bowing, and Pure Storage) are used for carrying out research work. The performance evaluation of the proposed technique has been carried out after processing it against practical video processing attacks Gaussian noise, sharpening, rotation, blurring, and JPEG compression.

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Yadav, Snehlata, and Namita Tiwari. "Privacy preserving data sharing method for social media platforms." PLOS ONE 18, no. 1 (January 20, 2023): e0280182. http://dx.doi.org/10.1371/journal.pone.0280182.

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Digital security as a service is a crucial aspect as it deals with user privacy provision and secure content delivery to legitimate users. Most social media platforms utilize end-to-end encryption as a significant security feature. However, multimedia data transmission in group communication is not encrypted. One of the most important objectives for a service provider is to send the desired multimedia data/service to only legitimate subscriber. Broadcast encryption is the most appropriate cryptographic primitive solution for this problem. Therefore, this study devised a construction called anonymous revocable identity-based broadcast encryption that preserves the privacy of messages broadcasted and the identity of legitimate users, where even revoked users cannot extract information about the user’s identity and sent data. The update key is broadcast periodically to non-revoked users, who can obtain the message using the update and decryption keys. A third-party can also revoke the users. It is proven that the proposed construction is semantically secure against IND-ID-CPA attacks and efficient in terms of computational cost and communication bandwidth.

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Satpathy, Rudra Bhanu, and J. Sunil Gavaskar. "Utilization of Social-Graph Analysis for Investigating Social Structures Through the Use of Networks and Graph Theory." Technoarete Transactions on Advances in Data Science and Analytics 1, no. 1 (February 22, 2022): 24–28. http://dx.doi.org/10.36647/ttadsa/01.01.a005.

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Social Network Analysis (SNA) is a digital process that helps to represent the collaboration between the individuals of society. Graphical representation of individual data has helped to understand social structure. Along with that, this research study has selected a secondary data collection method to understand the actual structure of the society. This graphical representation has included several nodes and edges of society to understand the interaction between the communities. In the present time, different organizations have faced difficulties due to a lack of communication. Along with that, graphical theory and network diagrams have helped to understand the weak ties and strong ties of society. According to that, society got the chance to improve the structure and build effective collaboration. Keyword :Social Network Analysis (SNA), nodes, edges, ties, interpersonal relationship

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Kim, Seongyong, Tae Hyeon Jeon, Ilsun Rhiu, Jinhyun Ahn, and Dong-Hyuk Im. "Semantic Scene Graph Generation Using RDF Model and Deep Learning." Applied Sciences 11, no. 2 (January 17, 2021): 826. http://dx.doi.org/10.3390/app11020826.

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Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches.

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Amato, Flora, Giovanni Cozzolino, and Giancarlo Sperlì. "A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks." Information 10, no. 6 (May 28, 2019): 183. http://dx.doi.org/10.3390/info10060183.

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Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach’s effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on.

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Gupta, B. B., and Somya Ranjan Sahoo. "Fake profile detection in multimedia big data on online social networks." International Journal of Information and Computer Security 12, no. 2/3 (2020): 303. http://dx.doi.org/10.1504/ijics.2020.10026785.

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Sahoo, Somya Ranjan, and B. B. Gupta. "Fake profile detection in multimedia big data on online social networks." International Journal of Information and Computer Security 12, no. 2/3 (2020): 303. http://dx.doi.org/10.1504/ijics.2020.105181.

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Ji, Xiangyang, Qifei Wang, Bo-Wei Chen, Seungmin Rho, C. C. Jay Kuo, and Qionghai Dai. "Online distribution and interaction of video data in social multimedia network." Multimedia Tools and Applications 75, no. 20 (November 25, 2014): 12941–54. http://dx.doi.org/10.1007/s11042-014-2335-1.

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Lee, Changhoon. "Guest Editorial: Automated Big Data Analysis for Social Multimedia Network Environments." Multimedia Tools and Applications 75, no. 20 (August 18, 2016): 12663–67. http://dx.doi.org/10.1007/s11042-016-3838-8.

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Zhu, Tieying, Shanshan Wang, Xiangtao Li, Zhiguo Zhou, and Riming Zhang. "Structural Attack to Anonymous Graph of Social Networks." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/237024.

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With the rapid development of social networks and its applications, the demand of publishing and sharing social network data for the purpose of commercial or research is increasing. However, the disclosure risks of sensitive information of social network users are also arising. The paper proposes an effective structural attack to deanonymize social graph data. The attack uses the cumulative degree ofn-hop neighbors of a node as the regional feature and combines it with the simulated annealing-based graph matching method to explore the nodes reidentification in anonymous social graphs. The simulation results on two social network datasets show that the attack is feasible in the nodes reidentification in anonymous graphs including the simply anonymous graph, randomized graph andk-isomorphism graph.

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Kucukkececi, Cihan, and Adnan Yazici. "Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big Data." IEEE Access 7 (2019): 67818–32. http://dx.doi.org/10.1109/access.2019.2918765.

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Journal articles on the topic 'Graph, social and multimedia data'

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