135 research outputs found
MULTIMEDIA SOCIAL NETWORKS
Nowadays, On-Line Social Networks represent an interactive platform to share -- and very often interact with -- heterogeneous content for different purposes (e.g to comment events and facts, express and share personal opinions on specific topics, and so on), allowing millions of individuals to create on-line profiles and communicate personal information.
In this dissertation, we define a novel data model for Multimedia Social Networks (MSNs), i.e. social networks that combine information on users -- belonging to one or more social communities -- with the multimedia content that is generated and used within the related environments. The proposed data model, inspired by hypergraph-based approaches, allows to represent in a simple way all the different kinds of relationships that are typical of these environments (among multimedia contents, among users and multimedia content and among users themselves) and to enable several kinds of analytics and applications.
Exploiting the feature of MSN model, the following two main challenging problems have been addressed: the Influence Maximization and the Community Detection. Regarding the first problem, a novel influence diffusion model has been proposed that, learning recurrent user behaviors from past logs, estimates the probability that a given user can influence the other ones, basically exploiting user to content actions. On the top of this model, several algorithms (based on game theory, epidemiological etc.) have been developed to address the Influence Maximization problem. Concerning the second challenge, we propose an algorithm that leverages both user interactions and multimedia content in terms of high and low-level features for identifying communities in heterogeneous network.
Finally, experimental analysis have been made on a real Multimedia Social Network ("Flickr") for evaluating both the feasibility of the model and the effectiveness of the proposed approaches for Influence Maximization and community detection
Multimedia Social Networks: Game Theoretic Modeling and Equilibrium Analysis
Multimedia content sharing and distribution over multimedia social networks is more popular now than ever before: we download music from Napster, share our images on Flickr, view user-created video on YouTube, and watch peer-to-peer television using Coolstreaming, PPLive and PPStream. Within these multimedia social networks, users share, exchange, and compete for scarce resources such as multimedia data and bandwidth, and thus influence each other's decision and performance. Therefore, to provide fundamental guidelines for the better system design, it is important to analyze the users' behaviors and interactions in a multimedia social network, i.e., how users interact with and respond to each other.
Game theory is a mathematical tool that analyzes the strategic interactions among multiple decision makers. It is ideal and essential for studying, analyzing, and modeling the users' behaviors and interactions in social networking. In this thesis, game theory will be used to model users' behaviors in social networks and analyze the corresponding equilibria. Specifically, in this thesis, we first illustrate how to use game theory to analyze and model users' behaviors in multimedia social networks by discussing the following three different scenarios. In the first scenario, we consider a non-cooperative multimedia social network where users in the social network compete for the same resource. We use multiuser rate allocation social network as an example for this scenario. In the second scenario, we consider a cooperative multimedia social network where users in the social network cooperate with each other to obtain the content. We use cooperative peer-to-peer streaming social network as an example for this scenario. In the third scenario, we consider how to use the indirect reciprocity game to stimulate cooperation among users. We use the packet forwarding social network as an example.
Moreover, the concept of ``multimedia social networks" can be applied into the field of signal and image processing. If each pixel/sample is treated as a user, then the whole image/signal can be regarded as a multimedia social network. From such a perspective, we introduce a new paradigm for signal and image processing, and develop generalized and unified frameworks for classical signal and image problems. In this thesis, we use image denoising and image interpolation as examples to illustrate how to use game theory to re-formulate the classical signal and image processing problems
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
Behavior Modeling and Forensics for Multimedia Social Networks
Within the past decades, the explosive combination of multimedia
signal processing, communications and networking technologies has
facilitated the sharing of digital multimedia data and enabled
pervasive digital media distribution over all kinds of networks.
People involved in the sharing and distribution of multimedia
contents form \emph{multimedia social networks} in which users
share and exchange multimedia content, as well as other resources.
Users in a multimedia social network have different objectives and
influence each other's decision and performance. It is of ample
importance to understand how users interact with and respond to
each other and analyze the impact of human factors on multimedia
systems. This thesis illustrates various aspects of issues and
problems in multimedia social networks via two case studies of
human behavior in multimedia fingerprinting and peer-to-peer live
streaming.
Since media security and content protection is a major issue in
current multimedia systems, this thesis first studies the user
dynamics of multimedia fingerprinting social networks. We
investigate the side information which improves the
traitor-tracing performance and provide the optimal strategies for
both users (fingerprint detector and the colluders) in the
multimedia fingerprinting social network. Furthermore, before a
collusion being successfully mounted, the colluders must be
stimulated to cooperate with each other and all colluders have to
agree on the attack strategy. Therefore, not all types of
collusion are possible. We reduce the possible collusion set by
analyzing the incentives and bargaining behavior among colluders.
We show that the optimal strategies designed based on human
behavior can provide more information to the fingerprint detector
and effectively improve the collusion resistance.
The second part of this thesis focuses on understanding modelling
and analyzing user dynamics for users in various types of
peer-to-peer live streaming social networks. We stimulate user
cooperation by designing the optimal, cheat-proof, and
attack-resistant strategies for peer-to-peer live streaming social
networks over Internet as well as wireless networks. Also, as more
and more smart-phone users subscribe to the live-streaming
service, a reasonable market price has to be set to prevent the
users from reselling the live video. We start from analyzing the
equilibrium between the users who want to resell the video and the
potential buyers to provide the optimal price for the content
owner
Are People Really Social on Porn 2.0?
Social Web 2.0 features have become a vital component in a variety of multimedia systems, e.g., Last.fm, Flickr and Spotify. Interestingly, adult video websites are also starting to adopt these Web 2.0 principles, giving rise to the term ``Porn 2.0''. This paper examines a large Porn 2.0 social network, through data covering 563k users. We explore a number of unusual behavioural aspects that set this apart from more traditional multimedia social networks, including differences in browsing activity, social communications and relationship creation. We also analyse the nature and behaviour of content sharing, highlighting the role it plays in the Porn 2.0 community, as well as the preferences that users have when deciding what to consume. We particularly explore the impact that gender and sexuality have on these issues, showing their vital importance for aspects such as profile popularity
A collaborative mobile architecture for multicast live-streaming social networks
Multimedia social network analysis is an emerging
research area, which analyzes the behaviour of users
who share multimedia content and investigates the
impact of human dynamics on multimedia systems. In
collaborative mobile networks, receivers cooperate
with each other to provide a distributed, highly
scalable and robust platform for live streaming
applications. However, every user wishes to use as
much bandwidth as possible to receive a high-quality
video; then, congestion control should be addressed.
This paper proposes a collaborative mobile
architecture to model receiver (in this case user)
behaviour using congestion control and reliable
strategies to stimulate user cooperation in multicast
live streaming. Thus, an author´s protocol named
Scalable Reliable Multicast Stair Hybrid (SRMSH) is
presented as new hybrid multiple layer mechanism for
multicast congestion control providing detection and
recovery loss. Simulation results show that the
proposed strategies can effectively stimulate user
cooperation, achieve cheat free and provide reliable
services within a mobile multimedia social network
Secure Watermarking for Multimedia Content Protection: A Review of its Benefits and Open Issues
Distribution channels such as digital music downloads, video-on-demand, multimedia social networks, pose new challenges to the design of content protection measures aimed at preventing copyright violations. Digital watermarking has been proposed as a possible brick of such protection systems, providing a means to embed a unique code, as a fingerprint, into each copy of the distributed content. However, application of watermarking for multimedia content protection in realistic scenarios poses several security issues. Secure signal processing, by which name we indicate a set of techniques able to process sensitive signals that have been obfuscated either by encryption or by other privacy-preserving primitives, may offer valuable solutions to the aforementioned issues. More specifically, the adoption of efficient methods for watermark embedding or detection on data that have been secured in some way, which we name in short secure watermarking, provides an elegant way to solve the security concerns of fingerprinting applications. The aim of this contribution is to illustrate recent results regarding secure watermarking to the signal processing community, highlighting both benefits and still open issues. Some of the most interesting challenges in this area, as well as new research directions, will also be discussed
Social Multimedia Networks Behaviour Model & Architecture
People constantly use social multimedianetworks to communicate with one another, with usersmostly sharing data, such as photos and videos. Weexamine the motivations that drive colluders to formalliances over social networking platforms anddetermine how these groups create coalitions toadvance their interests. We also investigate thenetwork architectures that underlie social multimedianetworks and how these platforms circulate. Sucharchitectures are connected to different protocols,including WebID, Semantic Pingback andPubSubHubbub, to form a logical semantic circulatingsocial multimedia network that delivers a centralisedsocial network structure
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