Features Of Online Social Networks Research Articles

Nifty method for prediction dynamic features of online social networks from users’ activity based on machine learning

Nowadays with the development of m4obile personal devices, the interaction of most people takes place through online social network more than ever. They rely on online applications to communicate, express their opinions, or react to others expressions instead of waiting the time to do that directly in a real life. Computationally, such interaction is modeled as a virtual network (or formally as graph) that is described with a set of features e.g. graph diameter, average-clustering coefficient etc. To compute these features, it is required to count or inspect all nodes or/and edges properties of the graph. When the graph is dynamic, i.e., the structure is change over time with each interaction, the computation of these features is a challenge and complex for time and space. Instead, AI based approaches are suggested to predict such features based on only few information of an interaction. This work trains the machine to learn computing the global features of online social networks through noticing the effect of users’ interaction on these features. Three datasets of different of two real online social networks are used in experiments. The obtained result shows an approximate accuracy of predication by RF 99% for Email interaction dataset and 82% by TreeNet for College message interaction dataset. The results of paper refer to: (a) The paper demonstrates the successful application of machine learning techniques to predict and analyze dynamic graph features in online social networks. (b) The Random Forest model emerges as the most suitable approach, providing accurate predictions and insights into the dynamics of dynamic graphs. (c) The study highlights the importance of the Graph Average Clustering Coefficient (GACC) as a significant feature in predicting global graph dynamics. (d) The research showcases the limitations of the KNN-Regression model and emphasizes the need for models that can handle the complexity and nonlinearity of dynamic graphs effectively.

Recurrent Neural Networks in Predicting the Popularity of Online Social Networks Content: A Review

An online social network is a web platform that individuals use to make social relationships with people who share similar interests, activities, connections, and backgrounds. All online social networks differ in the number of features they provide and their format. In recent years, drastic growth has been seen in the users of online social networks like Flickr, Instagram, Pinterest, Twitter, etc. Among all the features of online social networks, content sharing is the one being widely used by individual users and large organizations. Due to this, content popularity prediction has been extensively studied nowadays, considering various aspects related to it. The study throws light on the use of machine learning techniques in this field. Various algorithms have been used to handle popularity prediction, including classification, regression, and clustering techniques. It is feasible to extract the essential information from such content using machine learning algorithms and utilize the retrieved information in a variety of ways, the majority of which are commercial in nature. The goal of this study is to review and analyze various recurrent neural network (RNN) approaches for predicting the popularity of social media content.

Community detection for access-control decisions: Analysing the role of homophily and information diffusion in Online Social Networks

Access-Control Lists (ACLs) (a.k.a. “friend lists”) are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as “untrusted”) to the emerging communities inside the user’s ego-network. However, unlike users’ access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination.

Microblogging and the Value of Undirected Communication

Online social networks have become extremely popular, but what drives sharing through these channels? We demonstrate that one of the most popular features of online social networks, microblogging (e.g., tweeting or sharing Facebook status updates), is driven in part by its undirected nature. Microblogging allows people to simultaneously express themselves to a large number of potential communication partners without having to address anyone in particular. As a result, this communication channel is particularly valued when people feel socially apprehensive; it allows them to reach out without having to impose communication and potentially bother anyone in particular. These findings shed light on one reason why people use online social networks and provide insight into the value of undirected communication.

A Survey of Link Recommendation for Social Networks

Link recommendation has attracted significant attention from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which include “People You May Know” on LinkedIn and “You May Know” on Google+. In academia, link recommendation has been and remains a highly active research area. This article surveys state-of-the-art link recommendation methods, which can be broadly categorized into learning-based methods and proximity-based methods. We further identify social and economic theories, such as social interaction theory, that underlie these methods and explain from a theoretical perspective why a link recommendation method works. Finally, we propose to extend link recommendation research in several directions that include utility-based link recommendation, diversity of link recommendation, link recommendation from incomplete data, and experimental study of link recommendation.

Collaborative Filtering-Based Recommendation of Online Social Voting

Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix-factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath-based NN models outperform computation-intensive MF models in hot-voting recommendation, while users’ interests for nonhot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top- $k$ hit rate.

Support or competition? How online social networks increase physical activity: A randomized controlled trial

To identify what features of online social networks can increase physical activity, we conducted a 4-arm randomized controlled trial in 2014 in Philadelphia, PA. Students (n = 790, mean age = 25.2) at an university were randomly assigned to one of four conditions composed of either supportive or competitive relationships and either with individual or team incentives for attending exercise classes. The social comparison condition placed participants into 6-person competitive networks with individual incentives. The social support condition placed participants into 6-person teams with team incentives. The combined condition with both supportive and competitive relationships placed participants into 6-person teams, where participants could compare their team's performance to 5 other teams' performances. The control condition only allowed participants to attend classes with individual incentives. Rewards were based on the total number of classes attended by an individual, or the average number of classes attended by the members of a team. The outcome was the number of classes that participants attended. Data were analyzed using multilevel models in 2014. The mean attendance numbers per week were 35.7, 38.5, 20.3, and 16.8 in the social comparison, the combined, the control, and the social support conditions. Attendance numbers were 90% higher in the social comparison and the combined conditions (mean = 1.9, SE = 0.2) in contrast to the two conditions without comparison (mean = 1.0, SE = 0.2) (p = 0.003). Social comparison was more effective for increasing physical activity than social support and its effects did not depend on individual or team incentives.

When Online Visibility Deters Social Interaction: The Case of Digital Gifts

One of the defining features of online social networks is that users' actions are visible to other users. In this paper, we argue that such visibility can have a detrimental effect on users' willingness to exchange digital gifts. Gift giving is an intimate activity that comes with social risk, and the public nature of online environments can deter interactions that usually occur in smaller, more intimate settings. To study the effects of online visibility on the decision to give, we analyze a unique dataset from a large online social network that offers users the option of buying a digital gifting service. We find that purchase rates of the service increased with the number of ties that users kept on the network, but decreased with the extent to which those ties were connected to each other. We argue that the latter effect is due to the fact that, when a user's ties are connected, any gift sent between the user and one tie is visible to their mutual contacts. We explore how characteristics of users' networks moderate the effect of online visibility, and argue that firms should take consumer network structure into account when designing digital products and promoting engagement online.

When Online Visibility Deters Social Interaction: The Case of Digital Gifts

One of the defining features of online social networks is that users’ actions are visible to other users. In this paper, we argue that such visibility can have a detrimental effect on users’ willingness to exchange digital gifts. Gift giving is an intimate activity that comes with social risk, and the public nature of online environments can deter interactions that usually occur in smaller, more intimate settings. To study the effects of online visibility on the decision to gift, we analyze a unique dataset from a large online social network that offers users the option of buying a digital gifting service. We find that purchase rates of the service increased with the number of ties that users kept on the network, but decreased with the extent to which those ties were connected to each other. We argue that the latter effect is due to the fact that, when a user’s ties are connected, any gift sent between the user and one tie is visible to their mutual contacts. We explore how characteristics of users’ networks moderate the effect of online visibility, and argue that firms should take consumer network structure into account when designing digital products and promoting engagement online.

ECRModel: An Elastic Collision-Based Rumor-Propagation Model in Online Social Networks

With the rapid development of online social networks (OSN), the influence of rumor propagation on social life raises great concern. Traditional rumor-propagation models, which do not fully consider the features of OSN, are not suitable for use in OSN. In this paper, we focus on discovering a pattern of rumor-propagation phenomena in OSN, and propose a novel rumor-propagation model, inspired by a ball elastic-collision model, called the elastic collision-based rumor-propagation model (ECRModel). We investigate the dynamics of ball elastic collisions, which is similar to the dynamics of rumor propagation between nodes in OSN. We adopt the parameter relationships of the elastic collision model and apply them to rumor propagation in social networks. In the ECRModel, we do not directly adopt the node classification categories of “Ignorants, Spreaders, and Stiflers”, but divide the user nodes into three types: 1) inactive and never spread rumors; 2) active and spread rumors forward; and 3) inactive but have previously spread rumors. We mathematically model node interaction attributes, and analyze the spreading probabilities and the steady state, considering both individual perspectives with detailed attributes and integral perspectives with node-state densities. At last, we conduct a series of simulations, and the results verify the correctness of the analytical results. We further investigate the effects of detailed properties on rumor propagation, such as average out-degree of OSN, rumor confusingness degree, and each node’s comprehensive influence.

Overlapping Community Detection in Temporal Networks

Background/Objectives: One of the most commonly observed features of Online Social Networks is Community Structure. This feature provides great benefit focusing on insights of network structure, hidden patterns and the flow of information between actors. Methods/Statistical Analysis: Most real-world social networks are inherently dynamic, grow rapidly in terms of social interactions. These interactions in network are reflected by edges in a graph. Instead of updating a network structure for every edge change, the proposed method tracks the edges at every unique time stamp in a subgraph and modify the network only with the changed edges. Findings: There are many static community detection algorithms for discovering communities in networks, but very few deal with incremental structural changes in the network. The proposed algorithm DOMLPA (Dynamic Overlapping Multi-Label Propogation) deals with dynamic networks where data arrives as a stream to find the overlapping nodes in communities. To find the new edges the proposed algorithm lists out the differences in edges between the subgraph and the network for every snapshot. Based on the differences the label edges would be added or removed from the network and adjacency entries, neighbors’ list and label distribution entries are modified eventually. Speaker node function is activated to start the propogation process inorder to get the labels for every node. If a node contains a only one label it belongs to single community. If a node carries more than one label with more than one maximum probability entry, then it belongs to multiple communities. The opportunity to capture the evolutionary patterns in dynamic networks is lost by not considering the time of interaction in static algorithms. Application/Improvements: The study of communities is helpful in examining patterns leading to understand the structure of networks, finding the information flow and events taking place between a group of social actors over a period of time and to identify trending sentiments about brands based on tweets.

A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

Link recommendation has attracted significant attentions from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which include “People You May Know” on LinkedIn and “You May Know” on Google . In academia, link recommendation has been and remains a highly active research area. This paper surveys state-of-the-art link recommendation methods, which can be broadly categorized into learning-based methods and proximity-based methods. We further identify social and economic theories, such as social interaction theory, that underlie these methods and explain from a theoretical perspective why a link recommendation method works. Finally, we propose to extend link recommendation research in several directions that include utility-based link recommendation, diversity of link recommendation, link recommendation from incomplete data, and experimental study of link recommendation.

Are You Feeling Lonely? The Impact of Relationship Characteristics and Online Social Network Features on Loneliness

In contemporary society, many people move away from their personal networks for extended periods to reach professional and/or educational goals. This separation can often lead to feelings of loneliness, which can be stressful and sometimes debilitating for the individual. We seek to understand how a person’s use of online social networks (OSNs)—technology-enabled tools that assist users with creating and maintaining their relationships—might affect their perceptions of loneliness. Prior research has offered mixed results about how OSNs affect loneliness—reporting both positive and negative effects. We argue in this study that a clearer perspective can be gained by taking a closer look at how individuals approach their relationship management in OSNs. Building on theoretical works on loneliness, we develop a model to explain the effects of relationship characteristics (i.e., relationship orientation, self-disclosure, and networking ability) and OSN features (i.e., active or passive) on perceived loneliness. Our findings show that OSN can be linked to both more and less perceived loneliness, that is, individuals’ relationship orientation significantly affects their feelings of loneliness, which are further moderated by their degree of self-disclosure within the OSN. Furthermore, how users engage in the OSN (either actively or passively) influences their perceptions of loneliness. Practical implications regarding perceived loneliness include recommendations for firms to encourage mobile workers to utilize OSNs when separated from others, for education providers to connect with their new students before they arrive, and for users to utilize OSNs as a social bridge to others they feel close with.

Dynamic social community detection and its applications.

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.

Open Social Networking for Online Collaboration

Features of online social networks are being introduced in various applications and environments, including tools supporting virtual teams and online collaboration in general. In fact, social networking has a large potential for easing collaboration, also across organizational boundaries. However, effective e-collaboration through social networks requires the development of open and interoperable systems, allowing people belonging to different organizations to create ties, reflecting real connections existing in daily activities. Attention has to be paid to identity management, for allowing users to develop their reputation consistently, and to privacy and confidentiality, for creating a more trustful environment and protecting freedom of expression. In this article, the authors propose a novel peer-to-peer system, named Blogracy, leveraging the large BitTorrent network. Its main distinguishing features are: (i) protection of users' identity and privacy, (ii) absence of central control over published content; (iii) interoperability with other systems; and (iv) low costs and scalability of the infrastructure.

Are friends overrated? A study for the social news aggregator Digg.com

The key feature of online social networks (OSN) is the ability of users to become active, make friends and interact via comments, videos or messages with those around them. This social interaction is typically perceived as critical to the proper functioning of these platforms; therefore, a significant share of OSN research in the recent past has investigated the characteristics and importance of these social links, studying the networks’ friendship relations through their topological properties, the structure of the resulting communities and identifying the role and importance of individual members within these networks.In this paper, we present results from a multi-year study of the online social network Digg.com, indicating that the importance of friends and the friend network in the propagation of information is less than originally perceived. While we do note that users form and maintain a social structure along which information is exchanged, the importance of these links and their contribution is very low: users with even a nearly identical overlap in interests react on average only with a probability of 2% to information propagated and received from friends. Furthermore, in only about 50% of stories that became popular from the entire body of 10 million news we find evidence that the social ties among users were a critical ingredient to the successful spread. Our findings indicate the presence of previously unconsidered factors, the temporal alignment between user activities and the existence of additional logical relationships beyond the topology of the social graph, that are able to drive and steer the dynamics of such OSNs.