Heterogeneous Online Social Networks Research Articles

A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.

A discrete electronic word-of-mouth propagation model and its application in online social networks

The research of electronic word-of-mouth (eWOM) propagation and control in online social networks (OSN) is of great significance for the management of modern enterprises. In view of the characteristics of eWOM, we propose a discrete propagation model by considering the hedging effect between positive and negative eWOM. Then, based on this discrete model, the influence of OSN topology structure, the ratio and the degree of initial disseminators on eWOM spreading process are discussed through extensive simulation experiments. Lastly, we further explore the optimal control strategy of eWOM diffusion in OSN, under the enterprises’ cost constraints on the basis of above analysis. The results show that increasing the proportion of individuals who propagate positive eWOM is preferred in homogeneous OSN, while identifying the figure or medium with high influence power to disseminate positive eWOM is the priority choice in heterogeneous OSN.

Intelligent approaches for security technologies

Intelligent approaches for security technologies

Centrality in heterogeneous social networks for lurkers detection: An approach based on hypergraphs

SummaryNowadays, social networks provide users an interactive platform to create and share heterogeneous content for a lot of different purposes (eg, to comment events and facts, and express and share personal opinions on specific topics), allowing millions of individuals to create online profiles and share personal information with vast networks known and sometimes also unknown people. Knowledge about users, content, and relationships in a social network may be used for an adversary attack of some victims easily. Although a number of works have been done for data privacy preservation on relational data, they cannot be applied in social networks and in general for big data analytics. In this paper, we first propose a novel data model that integrates and combines information on users belonging to 1 or more heterogeneous online social networks, together with the content that is generated, shared, and used within the related environments, using an hypergraph data structure; then we implemented the most diffused centrality measures and also introduced a new centrality measure—based on the concept of “neighborhood” among users—that may be efficiently applied for a number of data privacy issues, such as lurkers and neighborhood attack prevention, especially in “interest‐based” social networks. Some experiments using the Yelp dataset are discussed.

Investigating effects of social media news sharing on the relationship between network heterogeneity and political participation

This study investigated how use of social media is associated with political participation by focusing on the effects of network heterogeneity and news use on social media. Specifically, we introduce the concept of news sharing as a potential determinant of participation.Our study found the presence of two sub-dimensions of news sharing: news externalizing (e.g., news posting) and news internalizing (e.g., news seeking). Both dimensions were found to have a distinct pathway to participation, with news externalizing affecting participation directly and news internalizing affecting participation indirectly through news externalizing.Additionally, our data revealed the moderating effects of news sharing, which indicates that heterogeneous online social networks may boost citizens’ participation when users are active in sharing news with others.

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

Cost-Effective and Scalable Image Matching Across Heterogeneous Online Social Networks

In this modern era, social media facilitates us in communicating with the people across the world. Sharing of photos on social networks is due to their addictive interest in receiving the likes from other users in the network to gain popularity. Users on social network upload nearly 1.8 billion photos every day. Malicious users try to gather the publicly available photos on social networks and use it for creating bogus accounts. To identify those anomalous users, the photos shared are collected and processed by the social network manager to classify the original person from the fake one. As there are billions of users in each social network, there are an enormous amount of photo uploads which leads to the problem of scalability, slower processing performance and execution speed. The primary objective of this paper is to identify the similar image for a given a query image on a large set of image datasets crawled from online social networks through the Internet. For handling the scalability incurred from large image data sets, the image matching computation is implemented in distributed computing Map Reduce framework. The face recognition involves supervised machine learning approach employing Computer vision algorithms, namely Fisher face, Eigenface and Local Binary Pattern Histogram (LBPH). The similarity functions are used to calculate the distance between the query image and image sequence in the Hadoop file system. Experiments use the trained data sets to find the least similarity measure. The results obtained show that LBPH provides better and accurate matching results compared to other two face recognition approaches.

Relationship Identification Across Heterogeneous Online Social Networks

In the era of the social web, many people manage their social relationships through various online social networking services. It has been found that identifying the types of social relationships among users in online social networks facilitates the marketing of products via electronic “word of mouth.” However, it is a great challenge to identify the types of social relationships, given very limited information in a social network. In this article, we study how to identify the types of relationships across multiple heterogeneous social networks and examine if combining certain information from different social networks can help improve the identification accuracy. The main contribution of our research is that we develop a novel decision tree initiated random walk model, which takes into account both global network structure and local user behavior to bootstrap the performance of relationship identification. Experiments conducted based on two real‐world social networks, Sina Weibo and Jiepang, demonstrate that the proposed model achieves an average accuracy of 92.0%, significantly outperforming other baseline methods. Our experiments also confirm the effectiveness of combining information from multiple social networks. Moreover, our results reveal that human mobility features indicating location categories, coincidence, and check‐in patterns are among the most discriminative features for relationship identification.

On Charactering of Word-of-Mouth Propagation in Heterogeneous Online Social Networks

Recent years have witnessed the explosive growth of research on online social networks (OSNs), which provide a perfect platform for observing the Word-of-Mouth (WOM) propagation. However, recent research regarded OSNs as homogeneous networks, while the real-world OSNs are heterogeneous. This paper considered the heterogeneity of OSNs, proposed a WOM propagation model called HOSN to simulate WOM propagation in different types of nodes in OSNs. Numerical simulation experiments based on agents have been extensively conducted, which show the mechanism of WOM propagation in heterogeneous online social networks. It can be seen from the experimental results that along with the WOM spreading and time passing, more and more users in OSNs review and know the WOM information. Moreover, from the network structure and user behavior perspective, some interesting findings are got. First, the number of initial known nodes has a certain impact on the WOM propagation scope and speed. Then, the number of neighbors has a great impact on the WOM propagation process and results. Finally, percentage of different user type is the decisive factor for WOM propagation either in final propagated number or propagation speed. Findings in this paper attempt to complement the theoretical framework of WOM propagation and complex networks, and thus to further promote the practice of WOM control in OSNs