Intelligent Social Networks Model Based On Semantic Tag Ranking

Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output

Multi-Agent Systems for Social Network Modelling

Práce poskytuje čitateli úvod do témat sociálních sítí a multi-agentních systémů. Jejím cílem je popsat návrh a implementaci funkčního modelu sociální sítě jakožto multi-agentního systému postaveného na frameworku Jason a na závěr zhodnotit tuto snahu.This thesis introduces the reader to topics of social networks and multi-agent systems. It's goal is to describe design and implementation of a functional model of social network as a multi-agent system built on Jason framework, and, in the end evaluate this effort.

Supporting Multi-agent Coordination and Computational Collective Intelligence in Enterprise 2.0 Platform

In this paper, we propose a novel approach utilizing a professional Social network (Pro Social Network) and a new coordination protocol (CordiNet). Our motivation behind this article is to convince Small and Medium Enterprises managers that current organizations have chosen to use Enterprise 2.0 tools because these latter have demonstrated remarkable innovation as well as successful collaboration and collective intelligence. The particularity of our work is that is allows employer to share diagnosis and fault repair procedures on the basis of some modeling agents. In fact, each enterprise is represented by a container of agents to ensure a secured and confidential information exchange between intra employers, and a central main container to connect all enterprises’ containers for a social information exchange. Enterprise’s container consists of a Checker Enterprise Agent (ChEA), a Coordinator Enterprise Agent (CoEA) and a Search Enterprise Agent (SeEA). Whereas the central main container comprises its proper agents such as Selection Agent (SA), and a Supervisor Agent (SuA). JADE platform is used to allow agents to communicate and collaborate. The FIPA-ACL performatives have been extended for this purpose. We conduct some experiments to demonstrate the feasibility of our approach

Social Network Analysis and Simulations

Tato diplomová práce se zabývá popisem, jakými způsoby je možné analyzovat sociální sítě, návrhem a implementací modelu, který simuluje konkrétní sociální síť, a jeho analýzou. V dnešní době jsou sociální sítě velmi vyhledávané a používané, proto jsou i častým cílem zkoumání. Práce se věnuje statické analýze sociálních sítí, kde vidíme sociální síť jako graf a zjišťujeme různé vlastnosti jednotlivých objektů v síti a podle toho určujeme, jak jsou tyto objekty pro síť významné. Zajímají nás i vztahy mezi entitami, protože mají vliv na šíření informací v síti. Strukturní vlastnosti sítě pak určují existenci různých komunit. V rámci dynamické analýzy modelujeme sociální síť pomocí multiagentních systémů, které jsou velmi vhodné pro znázornění změn v síti. Multiagentním systémem byl také implementován simulační model, který představuje konkrétní sociální síť. Jeho chování bylo analyzováno a zkoumáno pomocí vybraných metod.This diploma thesis is focusing on description of processing social network analysis, design and implementation of a model that simulates a particular social network and its analysis. Social networks are modern and very used in this time. They are very good point for exploring. This project deal with static analysis social network, where social network is constructed by graph. We nd out di erent properties of single component and than we establish signi cance of them. Relationships between components are important too for us, because they have a big influence on propagation information in network. Structural properties figure out existence of di fferent communities. We simulate social network with multi-agent systems, they are desirable for represent changes in network. Multi-agent systems have implemented a simulation model that represents a particular social network. His behaviour was analyzed and examinated by chosen methods.

Advanced Agent-Based Modeling for Social Networks

Agent-based modeling and simulation have been successfully applied to problems emerging from social sciences and could be profitably used also for the online social networks. However, the tools presently available for agent-based modeling do not offer specific support for social network models. In the present work, we present a unified conceptual framework to develop both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model to express the other models. In addition, we designed a domain-specific language to formulate the models in an executable way, so that simulations can be performed effortlessly. The language aims at being expressive and powerful for those with a strong background in computing, and yet simple and easy to learn for those with different expertises. We also developed a software platform that can execute such models in an agent-oriented context, providing effective support for large networks. Moreover, the platform hides most of the complexity of running the simulations on remote server-class machines. We validated out approach by translating several traditional models in our meta-model, verifying that the expected features of the models are maintained. The results show that our approach is successful in providing a friendly and easy environment to perform agent-based simulations over social networks, simulations that are of interest both to develop models and to study the results of the models themselves. Then, considering the favorable results we obtained, we applied our platform to the still open problem of creating an entirely distributed social networking system, which, as compared to the centralized ones, yields relevant advantages as far as privacy and resilience are concerned. We developed several models to help us in the understanding of the many issues that a P2P social networking system would have when deployed, and specifically of the well-known issue of the availability of rare resources. Through simulations, we found some criteria for the design of distributed social networks and some operation conditions which may result in a satisfactory user experience in terms of reduced delays in the propagation of information. Consequently, these results allow us to develop now a distributed social networking system optimized by means of our simulations

A Multi-Agent Implementation of Social Networks

In this paper, we present a multi-agent system implementing a fully distributed Social Network System supporting user profiles as FOAF profiles. This system is built around the idea that users should be the sole owners of the information they provide (either consciously or unconsciously) and addresses privacy issues by design, also minimizing the amount of information users have to disclose in order to make new friends. Users are represented by agents that both mediate access to private data and proactively negotiate with other agents in order to extend their user’s social network. We also present the distributed connection discovery algorithm used by the agents and detail the representation of data in the users’ profiles used to support the algorithm. The design is rather agnostic about the layer responsible for the communication technology; here we present a possible implementation on the top of the HDS software framework