Deep Learning-Based Community Detection Approach on Multimedia Social Networks

Exploiting multimedia data to analyze social networks has recently become one the most challenging issues for Social Network Analysis (SNA), leading to defining Multimedia Social Networks (MSNs). In particular, these networks consider new ways of interaction and further relationships among users to support various SNA tasks: influence analysis, expert finding, community identification, item recommendation, and so on. In this paper, we present a hypergraph-based data model to represent all the different types of relationships among users within an MSN, often mediated by multimedia data. In particular, by considering only user-to-user paths that exploit particular hyperarcs and relevant to a given application, we were able to transform the initial hypergraph into a proper adjacency matrix, where each element represents the strength of the link between two users. This matrix was then computed in a novel way through a Convolutional Neural Network (CNN), suitably modified to handle high data sparsity, in order to generate communities among users. Several experiments on standard datasets showed the effectiveness of the proposed methodology compared to other approaches in the literature.

Efficient Detection of Large-Scale Multimedia Network Information Data Anomalies Based on the Rule-Extracting Matrix Algorithm

With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is realized through the probability calculation, and the prior probability is calculated, to realize the detection of abnormally high network data. Practical results show that the rule-extracting matrix algorithm can effectively control the false positive rate of sample data, the detection accuracy is improved, and it has efficient detection performance.

The Transformation Of Marketing By Ai

From the ages, marketing has been a tough nut to crack as to how thoughtful to deal with the customer’s needs and to reach the specified products to them. Marketing is a vast area to deal with which is a crucial part of any business. In this decade we have a significant innovation to manage such issues effectively, which is Artificial Intelligence. Artificial Intelligence is quite possibly the most brilliant region of science today and can undoubtedly be utilized in the acts of marketing. Platforms for multimedia (social networks, news, images, video, Newsletters, infographics, podcasts, blogs, e-books.) are no longer accessible today are not just for the contact between users or users and companies, but also for companies to guide all aspects of business, collect and identify data of paramount importance. The artificial intelligence marketing technique has become climacteric for companies to find consumer conduct and needs. In this paper, I will walk you through the artificial intelligence marketing technique which transformed marketing into a whole new level. By the end of this paper, you will have a brief idea of how marketing has changed by knowing consumer conduct and needs using artificial intelligence.

Application of clustering algorithm in social network user scenario prediction

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Give Attention to Overlapping Network Detection in Networks for Multimedia

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.

A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks

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.