A Scalable Trust Management scheme for Mobile Ad Hoc Networks

Mobile ad hoc networks MANETs, have special resource requirements and different topology features, they establish themselves on fly without reliance on centralized or specialized entities such as base stations. All the nodes must cooperate with each other in order to send packets, forwarding packets, responding to routing messages, sending recommendations, among others, Cooperating nodes must trust each other. In MANETs, an untrustworthy node can wreak considerable damage and adversely affect the quality and reliability of data. Therefore, analyzing the trust level of a node has a positive influence on the confidence with which an entity conducts transactions with that node. This thesis presents a new trust management scheme to assign trust levels for spaces or nodes in ad hoc networks. The scheme emulates the human model which depends on the previous individual experience and on the intercession or recommendation of other spaces in the same radio range. The trust level considers the recommendation of trustworthy neighbors and their own experience. For the recommendation computation, we take into account not only the trust level, but also its accuracy and the relationship maturity. The relationship rationality -maturity-, allows nodes to improve the efficiency of the proposed model for mobile scenarios. We also introduce the Contribution Exchange Protocol (CEP) which allows nodes to exchange Intercessions and recommendation about their neighbors without disseminating the trust information over the entire network. Instead, nodes only need to keep and exchange trust information about nodes within the radio range. Without the need for a global trust knowledge. Different from most related works, this scheme improves scalability by restricting nodes to keep and exchange trust information solely with direct neighbors, that is, neighbors within the radio range. We have developed a simulator, which is specifically designed for this model, in order to evaluate and identify the main characteristics of the proposed system. Simulation results show the correctness of this model in a single-hop network. Extending the analysis to mobile multihop networks, shows the benefits of the maturity relationship concept, i.e. for how long nodes know each other, the maturity parameter can decrease the trust level error up to 50%. The results show the effectiveness of the system and the influence of main parameters in the presence of mobility. At last, we analyze the performance of the CEP protocol and show its scalability. We show that this implementation of CEP can significantly reduce the number messages

Augmenting Reputation-based Trust Metrics with Rumor-like Dissemination of Reputation Information

Abstract. Trust is an important and frequently studied concept in personal interactions and business ventures. As such, it has been examined by multitude of scientists in diverse disciplines of study. Over the past years, proposals have been made to model trust relations computationally, either to assist users or for modeling purposes in multi-agent systems. These models rely implicitly on the social networks established by participating entities (be they autonomous agents or internet users). At the same time, research in complex networks has revealed mechanisms of information diffusion, such as the spread of rumors in a population. By adapting rumor-spreading processes to reputation dissemination in multi-agent systems, this paper shows the benefit of augmenting an existing trust model with pro-actively, socially filtered trust information

Stochastic Sampling and Machine Learning Techniques for Social Media State Production

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries\u27 use of the global information environment. Our hope is that this type of simulation can be used to predict and understand the spread of misinformation, false narratives, fraudulent financial pump and dump schemes, and cybersecurity threats. To do this, our research team has created an agent-based model that can handle a variety of prediction tasks. This dissertation introduces a set of sampling and deep learning techniques that we developed to predict specific aspects of the evolution of online social networks that have proven to be challenging to accurately predict with the agent-based model. First, we compare different strategies for predicting network evolution with sampled historical data based on community features. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over different datasets. Second, we introduce a deep learning model for burst prediction. Bursts may serve as a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross-platform social media data is valuable for predicting bursts within a single social media platform. An LSTM model is proposed in order to capture the temporal dependencies and associations based upon activity information. These volume predictions can also serve as a valuable input for our agent-based model. Finally, we conduct an exploration of Graph Convolutional Networks to investigate the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of targeted graph convolutional networks. Graph Convolutional Networks are important in the social network context as the sociological and anthropological concept of \u27homophily\u27 allows for the method to use network associations in assisting the attribute predictions in a social network

On social and technical aspects of managing mobile Ad-hoc communities

Soziale Software beschreibt eine Klasse von Anwendungen, die es Benutzern erlaubt ueber das Internet mit Freunden zu kommunizieren und Informationen auszutauschen. Mit zunehmender Leistungsfaehigkeit mobiler Prozessoren verwandeln sich Mobiltelefone in vollwertige Computer und eroeffnen neue Moeglichkeiten fuer die mobile Nutzung sozialer Software. Da Menschen Mobiltelefone haeufig bei sich fuehren, koennen vergleichbare mobile Anwendungen staerker auf ihre unmittelbare Umgebungssituation zugeschnitten werden. Moegliche Szenarien sind die Unterstuetzung realer Treffen und damit verbundenen Mitgliederinteraktionen. Client-Server-Plattformen, die dabei haeufig zum Einsatz kommen wurden allerdings nie fuer solche hochflexiblen Gruppensituationen konstruiert. Mobile Encounter Netzwerke (MENe) verprechen hier mehr Flexibilitaet. Ein MEN stellt eine mobiler Peer-to-Peer-Plattformen dar, das ueber ein kurzreichweitiges Funknetz betrieben wird. Mit diesem Netzwerk werden Beitraege ueber einen raeumlichen Diffusionsprozess von einem mobilen Endgeraet zum naechsten verbreitet. Das hat zwei entscheidende Vorteile: Zunaechst ist der direkte Nachrichtenaustausch besser geeignet zur Verbreitung von situationsspezifischer Information, da die Informationsrelevanz mit ihrer Entfehrnung abnimmt. Gleichzeitig koennen aber auch Inhalte, die fuer einen breiten Interessenkreis bestimmt sind ueber Mitglieder mit herausragenden Mobilitaetscharakteristik in weit entfernte Gebiete transportiert werden. Ein Nachteil ist jedoch der hohe Ressourcenverbrauch. Zur Loesung dieses Problems entwickeln wir ein Rahmenwerk zur Unterstuetzung mobiler ad-hoc Gruppen, das es uns erlaubt, Gruppensynergien gezielt auszunutzen. Dieses Rahmenwerk bietet Dienstleistungen zur Verwaltung der Gruppendynamik und zur Verbreitung von Inhalten an. Mittels soziale Netzwerkanalyse wird die technische Infrastruktur ohne notwendige Benutzereingriffe kontinuierlich an die reale Umgebungssituation angepasst. Dabei werden moegliche Beziehungen zwischen benachbarten Personen anhand frueher Begegnungen analysiert, spontane Gruppenbildungen mit Clusterverfahren identifiziert und jedem Gruppenmitglied eine geeignete Rolle durch eine Positionsanalyse zugewiesen. Eine Grundvorraussetzung fuer eine erfolgreiche Kooperation ist ein effizienter Wissensaustausch innerhalb einer Gemeinschaft. Wie die Small World-Theorie zeigt, koennen Menschen Wissen auch dann effizient verbreiten, wenn ihre Entscheidung nur auf lokaler Umgebungsinformation basiert. Verschiedene Forscher machten sich das zu nutze, indem sie kurze Verbreitungspfade durch eine Verkettung hochvernetzter Mitglieder innerhalb einer Gemeinschaft konstruierten. Allerdings laesst sich dieses Verfahren nicht einfach auf MENe uebertragen, da die Transferzeit im Gegensatz zu dem drahtgebundenen Internet beschraenkt ist. Unser Ansatz beruht daher, auf der von Reagan et al. vorgestellten Least Effort Transfer-Hypothese. Diese Hypothese besagt, dass Menschen Wissen nur dann weitergeben, wenn sich der Aufwand zur Informationsuebertragung innerhalb bestimmter Grenzen bewegt. Eine erfolgreiche Wissensuebertragung haengt in diesem Fall vom Hintergrundwissen aller Beteiligter ab, was wiederum von unterschiedlichen kognitiven und sozialen Faktoren abhaengt. Entsprechend leiten wir ein Diffusionsverfahren ab, dass in der Lage ist, Inhalte in verschiedene Kompexitaetstufen einzuteilen und Datenuebertragungen an die vorgefundene soziale Situation anzupassen. Mit einem Prototyp evaluieren wir die Machbarkeit der Gruppen- und Informationsmanagementkomponente unseres Rahmenwerkes. Da Laborexperimente keinen ausreichenden Aufschluss ueber Diffusionseigenschaften im groesseren Massstab geben koennen, simulieren wir die Beitragsdiffusion. Dazu dient uns eine Verkehrsimulation, bei der Agenten zusaetzlich mit aktivitaetsbezogenen, sozialen und territorialen Modellen erweitern werden. Um eine realitaetsnahe Simulation zu gewaehrleisten, werden diese Modelle in Uebereinstimmung mit verschiedenen Studien zum Stadtleben generiert. Der technische Uebertragungsprozess wird anhand der Ergebnisse einer vorangegangenen Prototypuntersuchung parametrisiert. Waehrend eines Simulationslaufes bewegen sich Agenten auf einem Stadtplan und sammeln Kontakt- und Beitragsdaten. Analysiert man anschliessend die Netzwerktopologie auf Small World-Eigenschaften, so findet man eine Netzstruktur mit einer ausgepraegten Neigung zum Clustering (Freundschaftsnetzwerke) und einer ueberdurschnittlichen kurzen Weglaenge. Offensichtlich reicht die Alltagsmobilitaet aus, um ausreichend viele Verknuepfungen zwischen Gemeinschaftmitgliedern zu bilden. Die nachfolgende Diffusionsanalyse zeigt, dass vergleichbare Reichweiten wie bei einem flutungsbasierten Ansatz erzielt werden, allerdings mit anfaenglichen Verzoegerungen. Da unser Verfahren bei einem Ortswechsel die Anzahl der Informationsuebermittler auf zentrale Gruppenmitglieder begrenzt, steht mehr Bandbreite fuer den Datenaustausch zur Verfuegung. Herkoemliche Mitglieder (ohne Leitungsaufgaben) tauschen Inhalte vornehmlich in zeitunkritschen Situationen aus. Das hat den positiven Nebeneffekt, dass im Cache erheblich weniger Kopien aussortiert werden muessen. Wechselt man waehrend der Simulation die Beitragskategorie so erkennt man, dass zeitabhaengige Inhalte besser ueber regelmaessige Kontakte und zeitunabhaengig Inhalte durch zufaellige Kontakte verbreitet werden. Eine abschliessende Precision-Recall Analyse zeigt, dass herkoemmliche Gruppenmitglieder eine bessere Genauigkeit (Precision), und zentrale Mitglieder eine bessere Trefferquote (Recall) im Vergleich zu traditionellen Ansaetzen besitzen. Eine Erklaerung dafuer ist, dass der von uns gewaehlte gruppenbasierte Cacheansatz zu weniger Saeuberungszyklen aller Gruppenmitglieder fuehrt und somit nachhaltiger ausgerichtet ist.Social software encompasses a range of software systems that allow users to interact and share data. This computer-mediated communication has become very popular with social networking sites like Facebook and Twitter. The evolvement of smart phones toward mobile computers opens new possibilities to use social software also in mobile usage scenarios. Since mobile phones are permanently carried by their owners, the support focus is, however, much stronger set on promoting and augmenting real group gatherings. Traditional client-server platforms are not flexible enough to support complex and dynamic human encounter behavior. Mobile encounter networks (MENs) which represent a mobile peer-to-peer platform on top of a short range wireless network promise better flexibility. MENs diffuse content from neighbor-to-neighbor in a spatial diffusion process. For physical group gatherings this is advantageous for two reasons. Direct device-to-device interactions encourage sharing of situation-dependent content. Moreover, content is not necessarily locked within friend groups and may trigger networking effects by reaching larger audiences through user mobility. One disadvantage is, however, the high resource usage. We develop a social software framework for mobile ad-hoc groups, which partly solves this problem. This framework supports services for the management of group dynamics and content diffusion within and between groups. Social network analysis as an inherent part of the framework is used to adapt internal community states continuously with real world encounter situations. We hereby qualify interpersonal relationships based on encounter and communication statistics, identify social groups through incremental clustering and assign diffusion roles through position analysis. To achieve efficient content dissemination we make use of social diffusion phenomena. Other researchers have experimented extensively with the small world model as it proofs that people transfer knowledge based on local knowledge but are still capable of diffusing it efficiently on a global scale. Their approach is often based on identifying short paths through member connectivity. However, this scenario is not applicable in MENs as transfer time is limited in contrast to the wired Internet. Our approach is therefore based on the least effort transfer theory. Following Reagan et al., who first postulated this hypothesis, people transfer knowledge only if the transfer effort is within specific limits, which depends on different social and cognitive factors. We derive routing mechanisms, which are capable of distinguishing between different content complexities and apply information about peer's expertise and social network to identify advantageous paths and content transfers options. We evaluate the feasibility of the group management and content transfer component with prototypes. Since labor settings do not allow to obtain information about large scale diffusion experiences, we also conduct a multi-agent simulation to evaluate the diffusion capabilities of the system. Experiences from an earlier prototype implementation have been used to quantify the technical routing process. To emulate realistic community life, we assigned to each agent an individual daily agenda, social contacts and territory preferences specified according to outcomes from different urban city life surveys. During the simulation agents move on a city map according to these models and collect contact and content specific data. Analyzing the network topology according to small world characteristics shows a structure with a high tendency for clustering (friend networks) and a short average path length. Daily urban mobility creates enough opportunities to form shortcuts through the community. Content diffusion analysis shows that our approach reaches a similar amount of peers as network flooding but with delays in the beginning. Since our approach artificially limits the number of intermediates to central community peers more bandwidth is available during traveling and more content can be transferred as in the case of the flooding approach. Ordinary peers seem to have significantly fewer content replications if an unlimited cache is assumed proofing that our mechanism is more efficient. By varying the content type used during the simulation we recognize that time dependent content is better disseminated through frequent contacts and time independent content through random contacts. Performing a precision-recall analysis on peers caches shows that ordinary peers gain an overall better context precision, and central peers a better community recall. One explanation is that the shared cache approach leads to fewer content replacements in the cache as for instance the least recently used cache strategy

Graphs behind data: A network-based approach to model different scenarios

openAl giorno d’oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e più nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialità di affrontare con successo molti problemi aperti in diversi contesti. ​Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. ​INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc

Merchants In The Later Roman Empire

Merchants in the Later Roman Empire is an analysis of the social and economic lives of merchants, traders, and artisans in the 2nd to 4th centuries. It focuses, in particular, on the strategies adopted by merchants participating in small-scale local and regional trade and argues that concerns about social status were the primary determinants of merchant behavior. It expands the traditional application of New Institutional Economics to include informal and social institutions and considers how social norms limited and shaped merchant economic behavior. In doing so, the project moves discussions about the Roman economy away from the effect of the power, and particularly the institutional power, of the state toward a more dynamic model that accounts for the effect of interpersonal relations on the economy. Merchants in the Later Roman Empire argues that the Roman Empire rarely intended to regulate merchant activity in a comprehensive way and was more concerned with maintaining the status quo through its legislation and taxation. It contends that merchants engaged with the state at local levels where personal connections were critical. These ties were structured along similar lines to those between merchants and their peers, competitors, and customers—in short, to the connections they had with individuals throughout their communities. Taking reputation as its focus, this project argues for the institutional power of social norms in merchant social and economic life and analyzes the strategies used by merchants to present and advance themselves. Merchants invested heavily in their reputations and attempted to display their contributions to society, their good characters, and their success in business. These efforts were costly and every form of self-representation relied heavily on the disposition of the audiences to which they were directed. Merchants in the Later Roman Empire considers both the projection and the reception of reputation to conclude that these social norms constrained merchant actions in ways that limited their indiscriminate pursuit of profit but also generated economic opportunities by fostering trust and reducing market volatility

Journal of Telecommunications and Information Technology, nr 4

kwartalni

Social Media Strategies to Increase Professional Membership Association Dues Income

Social media strategies can play an important and specific role in nonprofits by increasing membership dues income. However, insufficient data exists supporting effective strategies for social media in professional membership associations, potentially affecting both business managers within those associations and the stakeholders they represent. The purpose of this qualitative single case study was to explore social media strategies that nonprofit marketing professionals used to increase professional membership association dues income. The sample included 7 nonprofit marketing professionals in a New Jersey-based professional membership association. Social exchange theory and social influence theory formed the conceptual framework. Data collection occurred using semistructured interviews, a review of campaign data, review of social media, and directly observing a social media planning meeting. Yin\u27s 5 steps for qualitative data analysis formed a logical and sequential process for analysis. An overarching theme, actionable strategies to increase dues income in professional membership associations, emerged with 5 themes, including engagement with social media connections; a comprehensive, coordinated strategy; and establishing trust. The implications for positive social change include the potential to help professional membership associations to increase revenue and better support their social advocacy missions, which could include an improvement in community service activities, benefiting both association members and small and large communities across the United States