Academic literature on the topic 'Online social networks – Mathematical models'
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Journal articles on the topic "Online social networks – Mathematical models"
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Bonato, Anthony, Noor Hadi, Paul Horn, Paweł Prałat, and Changping Wang. "Models of Online Social Networks." Internet Mathematics 6, no. 3 (January 2009): 285–313. http://dx.doi.org/10.1080/15427951.2009.10390642.
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Jiang, Ping, and Xiangbin Yan. "Stability analysis and control models for rumor spreading in online social networks." International Journal of Modern Physics C 28, no. 05 (March 9, 2017): 1750061. http://dx.doi.org/10.1142/s0129183117500619.
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This paper establishes a novel Susceptible-Infected-Removed (SIR) rumor spreading model for online social networks (OSNs). The model utilizes the node degree to describe the dynamic changes of the number of rumor spreaders and it can be regarded as an extension of the traditional SIR model. Stability analysis of the model reveals that the spreader in social networks has a basic reproduction number. If the basic reproduction number is less than 1, then rumors will disappear. Otherwise, rumors will persist. According to this result, we can predict the trend of rumor spreading. Then we propose an immune-structure SIR model to explore the control method of rumor spreading. Stability analysis and numerical simulation of the model indicate that immunizing susceptible individual is an effective method to control rumors. Further, the immune-structure model explains that the network structure decides the choice of immune methods. Our findings offer some new insights to control the spread of rumors on OSNs.
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Gabdrakhmanova, Nailia, and Maria Pilgun. "Intelligent Control Systems in Urban Planning Conflicts: Social Media Users’ Perception." Applied Sciences 11, no. 14 (July 17, 2021): 6579. http://dx.doi.org/10.3390/app11146579.
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The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts. The material for the study was data from social networks, microblogging, blogs, instant messaging, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the construction of the North-Eastern Chord (NEC) in Moscow (RF). To analyze the content of social media, a multimodal approach was used. The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perceptions based on their digital footprints. Artificial neural networks, differential equations, and mathematical statistics were involved in building the models. Differential equations of dynamic systems were based on observations enabled by machine learning. Mathematical models were developed to quickly detect, prevent, and address conflicts in urban planning in order to manage urban systems efficiently. In combination with mathematical and neural network model the developed approaches, made it possible to draw a conclusion about the tense situation around the construction of the NEC, identify complaints of residents to constructors and city authorities, and propose recommendations to resolve and prevent conflicts. Research data could be of use in solving similar problems in sociology, ecology, and economics.
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Govindankutty, Sreeraag, and Shynu Padinjappurathu Gopalan. "SEDIS—A Rumor Propagation Model for Social Networks by Incorporating the Human Nature of Selection." Systems 11, no. 1 (December 29, 2022): 12. http://dx.doi.org/10.3390/systems11010012.
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The explosive evolution of the internet has paved the path for the rise of social networks, which can help people connect remotely. Currently, social networks are commonly used for sharing thoughts, feelings, information, and personal life, which vary from individual to individual. The world has witnessed a tremendous increase in social media usage in the last decade, and more people are expected to spend their time online after the COVID-19 pandemic. This increases the rapid propagation of rumors and fake news within societies and communities. On one end, social networks act as an excellent platform for digital marketing and sharing information. However, on the other end, social network rumors and fake news create a significant impact on society, including riots. To study and analyze social network rumors, several mathematical rumor propagation epidemic models have been proposed. The majority are related to disease-spreading epidemic models and reject the human aspect of social selection. This paper introduces a new mathematical rumor propagation model for social networks by incorporating the human psychological aspect of selection as a separate state. Our mathematical analysis and computational simulation proved that the model exists within the system. It was also proven that the system is always non-negative and there always exists a solution in the system. Our implementation of an intervention mechanism within the discrete compartmental model simulation proved the necessity of an effective interference that can help to prevent the implications of uncontrolled rumor dissemination within social networks.
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Gnedash, Anna, and Veronika Katermina. "Abortion Ban in English Social Media in 2022: Pragmatic Linguistics of Online Communications." Virtual Communication and Social Networks 2022, no. 4 (December 22, 2022): 172–78. http://dx.doi.org/10.21603/2782-4799-2022-1-4-172-178.
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The present research featured the conflict discourse of online communication in social networks on the topic of abortion ban. The empirical base included 3,000,000 Twitter messages in English. The sampling by keywords women and abortion covered the period from May 1 to July 31, 2022, which saw an outburst of online and offline civic activities regarding some national anti-abortion policies. The resulting web corpus of network linguistic data (datasets) was subjected to multidimensional analysis using such methods as Data Science, mathematical modeling, relational sociology, corpus analysis, discourse analysis, etc. All these procedures combined resulted in a multidimensional comprehensive analysis of the simulated English asynchronous multimodal discursive field in Twitter. The models made it possible to visualize online communications in social networks, as well as to describe the discourse of online communication between pro-choice and pro-life. The authors analyzed the pragmatic potential of network communities on the current political agenda. The method might help to identify the conflict potential that can evolve from online communication into offline socio-political actions.
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DU, FANG, QI XUAN, and TIE-JUN WU. "EMPIRICAL ANALYSIS OF ATTENTION BEHAVIORS IN ONLINE SOCIAL NETWORKS." International Journal of Modern Physics C 21, no. 07 (July 2010): 955–71. http://dx.doi.org/10.1142/s0129183110015592.
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Studying attention behavior has its social significance because such behavior is considered to lead the evolution of the friendship network. However, this type of behavior in social networks has attracted relatively little attention before, which is mainly because, in reality, such behaviors are always transitory and rarely recorded. In this paper, we collected the attention behaviors as well as the friendship network from Douban database and then carefully studied the attention behaviors in the friendship network as a latent metric space. The revealed similar patterns of attention behavior and friendship suggest that attention behavior may be the pre-stage of friendship to a certain extent, which can be further validated by the fact that pairwise nodes in Douban network connected by attention links beforehand are indeed far more likely to be connected by friendship links in the near future. This phenomenon can also be used to explain the high clustering of many social networks. More interestingly, it seems that attention behaviors are more likely to take place between individuals who have more mutual friends as well as more different friends, which seems a little different from the principles of many link prediction algorithms. Moreover, it is also found that forward attention is preferred to inverse attention, which is quite natural because, usually, an individual must be more interested in others that he is paying attention to than those paying attention to him. All of these findings can be used to guide the design of more appropriate social network models in the future.
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Attri, Vikas. "Comparative study of Existing Models for Online Social Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 483–90. http://dx.doi.org/10.17762/turcomat.v12i2.856.
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Today, Online Social Networks becomes the first choice for businesses to broadcast their campaigns for branding, publicity, strategies, advertising, marketing, social influence and so many other areas. Social Network is a platform for communicating with social actors and Social Media is used by companies for broadcasting information. Online Social Network used by businesses for number of purposes but the primary concern is build new social connections that helps to target most audiences for successful campaign purposes. In OSNs sites the social objects are represented by nodes and the term edge used for connection between nodes under the graph theory. Today Social Network sites have becomes most exploded as compared to traditional sites because of impact of so many influence models over traditional models.Some of popular websites of OSN such as MySpace, Facebook, Flickr, YouTube, Google Video, Orkut, LinkedIn, Live Journal and BlogSpot have great impact on customer when targeting the sales marketing funnel for businesses. Adjacent users sometimes called engaged users tend to have more trust level as compared to random pairs users on the social media sites. Already have so much research that helps to calculate the trust factor using influence modeling. So influence models play a vital role to predict the behavior of the customer that helps to fulfill the goal of the business. The key contribution of this work is study of online social networking models.
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Luo, Peng, Chong Wu, and Yongli Li. "Link prediction measures considering different neighbors’ effects and application in social networks." International Journal of Modern Physics C 28, no. 03 (March 2017): 1750033. http://dx.doi.org/10.1142/s0129183117500334.
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Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.
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Liu, Xiaoyang, Chao Liu, and Xiaoping Zeng. "Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling." Complexity 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/5857372.
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Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.
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Kuzmenko, O., H. Yarovenko, and L. Skrynka. "ANALYSIS OF MATHEMATICAL MODELS FOR COUNTERING CYBER FRAUD IN BANKS." Vìsnik Sumsʹkogo deržavnogo unìversitetu 2022, no. 2 (2022): 111–20. http://dx.doi.org/10.21272/1817-9215.2022.2-13.
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The article is devoted to the current topic of analysis of mathematical models for countering cyber fraud in banks. This problem is due to the security risks growth in the banking system, which are formed by fraudsters' cyberattacks and cybercrimes implementation. Therefore, the priority task for cyberbanking security is the application of modern mathematical methods to analyse the sources of cyber attacks, identify threats and losses in the banking services market, identify cyber-attacks and assess the scenario of potential cyber risk, etc. The article analyses the most widespread types of cyber fraud: social engineering, phishing, stalking, farming, DoS attacks, online fraud, potentially unwanted programs, etc. The study also considered a model of cognitive computing and detection of suspicious transactions in banking cyber-physical systems based on quantum computing in BCPS for the post-quantum era. The advantages, disadvantages and results of the model are defined. Predictive modelling is proposed to detect fraud in real-time by analysing incoming bank transactions with payment cards. Within the framework of this method, such models are used for the classification of fraud detection as logistic regression, a decision tree, and a narrower technique - a random forest decision tree. The study also considered using the harmonic search algorithm in neural networks to improve fraud detection in the banking system. It is found that although this model has the advantage of learning ability based on past behaviour, there are difficulties in the long-term processing of many neural networks. The stages of model implementation are also given. In addition, the modelling of credit card fraud detection is based on using two types of models: supervised and unsupervised. Supervised models include logistic regression, K-nearest neighbours, and extreme gradient boosting. The one-class support vector model, restricted Boltzmann model, and generative-competitive network are considered among uncontrolled generative models.
Dissertations / Theses on the topic "Online social networks – Mathematical models"
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Tang, Hon Cheong 1980. "Gravity-based trust model for web-based social networks." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=112366.
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Web-based social networks have become one of the most popular applications on the Internet in recent years. However, most of the social networks rely on some simplistic trust models to manage trust information of the users, which can cause problems ranging from unsatisfied user experience to exposure to malicious users. This thesis proposes a gravity-based trust model to enhance the aggregation of personal trust information into a subjective reputation system. This new model maps all users on the social network into n-dimensional Euclidean spaces based on their direct trust information, and creates a trust social neighborhood for each user. The reputation of a target user is determined by applying gravity model to the information from both target's and observer's trust social neighborhood. A prototype of this trust model is implemented in order to evaluate the effects of varying different parameters of the gravity-based trust model.
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Bao, Qing. "Inferring diffusion models with structural and behavioral dependency in social networks." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/305.
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Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected nodes in social and information networks is referred to as diffusion. In the literature, a number of diffusion models have been proposed for different applications like influential user identification and personalized recommendation. However, comprehensive studies to discover the hidden diffusion mechanisms governing the information diffusion using the data-driven paradigm are still lacking. This thesis research aims to design novel diffusion models with the structural and behaviorable dependency of neighboring nodes for representing social networks, and to develop computational algorithms to infer the diffusion models as well as the underlying diffusion mechanisms based on information cascades observed in real social networks. By incorporating structural dependency and diversity of node neighborhood into a widely used diffusion model called Independent Cascade (IC) Model, we first propose a component-based diffusion model where the influence of parent nodes is exerted via connected components. Instead of estimating the node-based diffusion probabilities as in the IC Model, component-based diffusion probabilities are estimated using an expectation maximization (EM) algorithm derived under a Bayesian framework. Also, a newly derived structural diversity measure namely dynamic effective size is proposed for quantifying the dynamic information redundancy within each parent component. The component-based diffusion model suggests that node connectivity is a good proxy to quantify how a node's activation behavior is affected by its node neighborhood. To model directly the behavioral dependency of node neighborhood, we then propose a co-activation pattern based diffusion model by integrating the latent class model into the IC Model where the co-activation patterns of parent nodes form the latent classes for each node. Both the co-activation patterns and the corresponding pattern-based diffusion probabilities are inferred using a two-level EM algorithm. As compared to the component-based diffusion model, the inferred co-activation patterns can be interpreted as the soft parent components, providing insights on how each node is influenced by its neighbors as reflected by the observed cascade data. With the motivation to discover a common set of the over-represented temporal activation patterns (motifs) characterizing the overall diffusion in a social network, we further propose a motif-based diffusion model. By considering the temporal ordering of the parent activations and the social roles estimated for each node, each temporal activation motif is represented using a Markov chain with the social roles being its states. Again, a two-level EM algorithm is proposed to infer both the temporal activation motifs and the corresponding diffusion network simultaneously. The inferred activation motifs can be interpreted as the underlying diffusion mechanisms characterizing the diffusion happening in the social network. Extensive experiments have been carried out to evaluate the performance of all the proposed diffusion models using both synthetic and real data. The results obtained and presented in the thesis demonstrate the effectiveness of the proposed models. In addition, we discuss in detail how to interpret the inferred co-activation patterns and interaction motifs as the diffusion mechanisms under the context of different real social network data sets.
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Botha, Leendert W. "Modeling online social networks using Quasi-clique communities." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/17859.
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Thesis (MSc)--Stellenbosch University, 2011ENGLISH ABSTRACT: With billions of current internet users interacting through social networks, the need has arisen to analyze the structure of these networks. Many authors have proposed random graph models for social networks in an attempt to understand and reproduce the dynamics that govern social network development. This thesis proposes a random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network. Our approach explicitly models the tendency of communities to overlap, and also proposes a method for determining the probability of two users being connected based on their levels of commitment to the communities they both belong to. Previous community-based models do not incorporate community overlap, and assume mutual members of any community are automatically connected. We provide a method for fitting our model to real-world social networks and demonstrate the effectiveness of our approach in reproducing real-world social network characteristics by investigating its fit on two data sets of current online social networks. The results verify that our proposed model is promising: it is the first community-based model that can accurately reproduce a variety of important social network characteristics, namely average separation, clustering, degree distribution, transitivity and network densification, simultaneously.
AFRIKAANSE OPSOMMING: Met biljoene huidige internet-gebruikers wat deesdae met behulp van aanlyn sosiale netwerke kommunikeer, het die analise van hierdie netwerke in die navorsingsgemeenskap toegeneem. Navorsers het al verskeie toevalsgrafiekmodelle vir sosiale netwerke voorgestel in ’n poging om die dinamika van die ontwikkeling van dié netwerke beter te verstaan en te dupliseer. In hierdie tesis word ’n nuwe toevalsgrafiekmodel vir sosiale netwerke voorgestel wat ’n gemeenskapsgebaseerde benadering volg, deurdat gebruikers se verbintenisse aan gemeenskappe eksplisiet gemodelleer word, en dié gemeenskapsmodel dan in ’n sosiale netwerk omskep word. Ons metode modelleer uitdruklik die geneigdheid van gemeenskappe om te oorvleuel, en verskaf ’n metode waardeur die waarskynlikheid van vriendskap tussen twee gebruikers bepaal kan word, op grond van hulle toewyding aan hulle wedersydse gemeenskappe. Vorige modelle inkorporeer nie gemeenskapsoorvleueling nie, en aanvaar ook dat alle lede van dieselfde gemeenskap vriende sal wees. Ons verskaf ’n metode om ons model se parameters te pas op sosiale netwerk datastelle en vertoon die vermoë van ons model om eienskappe van sosiale netwerke te dupliseer. Die resultate van ons model lyk belowend: dit is die eerste gemeenskapsgebaseerde model wat gelyktydig ’n belangrike verskeidenheid van sosiale netwerk eienskappe, naamlik gemiddelde skeidingsafstand, samedromming, graadverdeling, transitiwiteit en netwerksverdigting, akkuraat kan weerspieël.
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Morales, Matamoros Javier. "On-line norm synthesis for open Multi-Agent systems." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/396133.
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Multi Agent Systems (MAS) are computerised systems composed of autonomous software agents that interact to solve complex problems. Within a MAS, agents require some mechanism to coordinate their activities. In the MAS literature, norms have been widely used to coordinate agents’ activities. Thus, given a MAS, a major research challenge is how to synthesise a normative system, namely a collection of norms, which supports its agents’ coordination.
This dissertation focuses on the automated synthesis of norms for open Multi- Agent Systems. In an open MAS, the agent population may change along time, agents may be developed by third parties and their behaviours are not known beforehand. These particular conditions make specially challenging to synthesise a normative system to govern an open MAS.
The MAS literature has mainly investigated two general approaches to norm synthesis: off-line design, and on-line synthesis. The first approach aims at synthesising a normative system at design time. With this aim, it assumes that the MAS state space is known at design time and does not change at runtime. This goes against the nature of open MAS, and thus off-line design is not appropriate to synthesise their norms. Alternatively, on-line norm synthesis considers that norms are synthesised at runtime. Most on-line synthesis research has focused on norm emergence, which considers that agents synthesise their own norms, thus assuming that they have norm synthesis capabilities. Again, this cannot be assumed in open MAS.
Against this background, this dissertation introduces a whole computational framework to perform on-line norm synthesis for open Multi-Agent Systems. Firstly, this framework provides a computational model to synthesise norms for a MAS at runtime. Such computational model requires neither knowledge about agents’ behaviours beforehand nor their participation in the norm synthesis pro- cess. Instead, it considers a regulatory entity that observes agents’ interactions at runtime, identifying situations that are undesirable for coordination to sub- sequently synthesise norms that regulate these situations. Our computational model has been conceived to be of general purpose so that it can be employed to synthesise norms in a wide range of application domains by providing little domain-dependent information. Secondly, our framework provides an abstract architecture to implement such regulatory entity (the so-called Norm Synthesis Machine), which observes a MAS and executes a synthesis strategy to synthe- sise norms. Thirdly, our framework encompasses a family of norm synthesis strategies intended to be executed by the Norm Synthesis Machine. Overall, this family of strategies supports multi-objective on-line norm synthesis
Our first synthesis strategy, the so-called base, aims at synthesising effective normative systems that successfully avoid situations that are undesirable for a MAS’ coordination. Then, two further strategies (called iron and simon) go beyond effectiveness and also consider compactness as a norm synthesis goal. iron and simon take alternative approaches to synthesise compact normative systems that, in addition to effectively achieve coordination, are as synthetic as possible. This allows them to reduce agents’ computational efforts when reasoning about norms. A fourth strategy, the so-called lion, goes beyond effectiveness and compactness to also consider liberality as a synthesis goal. lion aims at synthesising normative systems that are effective and compact while preserving agents’ freedom to the greatest possible extent. Our final strategy is desmon, which is capable of synthesising norms by considering different degrees of reactivity. desmon allows to adjust the amount of information that is required to decide whether a norm must be included in a normative system or not. Thus, desmon can synthesise
norms either by being reactive (i.e., by considering little information), or by being more deliberative (by employing more information).
We provide empirical evaluations of our norm synthesis strategies in two application domains: a road traffic domain, and an on-line community domain. In this former domain, we employ these strategies to synthesise effective, compact and liberal normative systems that successfully avoid collisions between cars. In the latter domain, our strategies synthesise normative systems based on users’ complaints about inappropriate contents. In this way, our strategies implement a regulatory approach that synthesises norms when there is enough user consensus about the need for norms.
Overall, this thesis advances in the state of the art in norm synthesis by providing a novel computational model, an abstract architecture and a family of strategies for on-line norm synthesis for open Multi-Agent Systems.Els sistemes Multi-Agent (MAS) són sistemes computeritzats composats d’agents autònoms que interaccionen per resoldre problemes complexos. A un MAS, els agents requereixen algun mecanisme per a coordinar les seves activitats. A la literatura en Sistemes Multi-Agent, les normes han estat àmpliament utilitzades per coordinar les activitats dels agents. Per tant, donat un MAS, un dels majors reptes d’investigació és el de sintetizar el sistema normatiu, és a dir, la col·lecció de normes, que suporti la coordinació dels agents. Aquesta tesi es centra en la síntesi automàtica de normes per sistemes Multi-Agent oberts. A un MAS obert, la població d’agents pot canviar amb el temps, els agents poden ésser desenvolupats per terceres parts, i els comportaments dels agents són desconeguts per endavant. Aquestes condicions particulars fan especialment complicat sintetizar el sistema normatiu que reguli un sistema Multi-Agent obert. En general, la literatura en Sistemes Multi-Agent ha investigat dues aproximacions a la síntesi de normes: disseny off-line, i síntesi on-line. La primera aproximació consisteix a sintetizar un sistema normatiu en temps de disseny. Amb aquest propòsit, aquesta aproximació assumeix que l’espai d’estats d’un MAS és conegut en temps de disseny i no canvia en temps d’execució. Això va contra la natura dels sistemes Multi-Agent oberts, i per tant el disseny off-line no és apropiat per a sintetitzar les seves normes. Com a alternativa, la síntesi on-line considera que les normes són sintetizades en temps d’execució. La majoria de recerca en síntesi on-line s’ha centrat en la emergència de normes, que considera que els agents sintetizen les seves pròpies normes, per tant assumint que tenen la capacitat de sintetitzar-les. Aquestes condicions tampoc no es poden assumir en un MAS obert. Donat això, aquesta tesi introdueix un marc computacional per la síntesi on-line de normes en sistemes Multi-Agent oberts. Primer, aquest marc proveeix un model computacional per sintetizar normes per un MAS en temps d’execució. Aquest model computacional no requereix ni coneixement sobre els comportaments dels agents per endavant ni la seva participación en la síntesi de normes. En canvi, considera que una entitat reguladora observa les interaccions dels agents en temps d’execució, identificant situacions indesitjades per la coordinació i sintetizant normes que regulen aquestes situacions. El nostre model computacional ha estat dissenyat per a ésser de propòsit general per tal que pugui ser utilitzat a la síntesi de normes en un ampli ventall de dominis d’aplicació proporcionant només información clau sobre el domini. Segon, el nostre marc proveeix una arquitectura abstracta per implementar aquesta entitat reguladora, anomenada Màquina de Síntesi, que observa un MAS en temps d’execució i executa una estratègia de síntesi que s’encarrega de sintetizar normes. Tercer, el nostre marc incorpora una familia d’estratègies de síntesi destinades a ésser executades per una màquina de síntesi. En general, aquesta familia d’estratègies soporta la síntesi multi-objectiu i on-line de normes. La nostra primera estratègia, anomenada BASE, està dissenyada per sintetitzar sistemes normatius eficaços que evitin de manera satisfactòria situacions indesitjades per la coordinació d’un sistema Multi-Agent. Després, dues estratègies de síntesi, anomenades IRON i SIMON, van més enllà de la eficàcia i també consideren la compacitat com a objectiu de síntesi. IRON i SIMON prenen aproximacions alternatives a la síntesi de sistemes normatius compactes que, a més d’aconseguir la coordinació de manera efectiva, siguin tant sintètics com fos possible. Això permet a aquestes estratègies reduir els esforços computacionals dels agents a l’hora de raonar sobre les normes. Una quarta estratègia, anomenada LION, va més enllà de la eficàcia i la compacitat per considerar també la liberalitat com a objectiu de síntesi. Lion sintetitza sistemes normatius que són eficaços i compactes mentre preserven la llibertat dels agents tant com sigui possible. La nostra última estratègia és desmon, que és capaç de sintetizar normes considerant diferents graus de reactivitat. desmon permet ajustar la quantitat d’informació necessària per decidir si una norma cal que sigui o no inclosa a un sistema normatiu. DESMON pot sintetizar normes essent reactiu (considerant poca informació), o essent més deliberatiu (considerant més informació). En aquesta tesi presentem avaluacions empíriques de les nostres estratègies de síntesi en dos dominis d’aplicació: el domini del tràfic, i el domini de les comunitats on-line. En aquest primer domini, utilitzem les nostres estratègies per a sintetizar sistemes normatius eficaços, compactes i liberals que eviten colisions entre cotxes. Al segon domini, les nostres estratègies sintetizen sistemes normatius basant-se en les queixes dels usuaris de la comunitat sobre continguts inapropiats. D’aquesta manera, les nostres estratègies implementen un mecanisme de regulació que sintetiza normes quan hi ha suficient consens entre els usuaris sobre la necessitat de normes. Aquesta tesi avança en l’estat de l’art en síntesi de normes al proporcionar un novedós model computacional, una arquitectura abstracta i una familia d’estratègies per la síntesi on-line de normes per sistemes Multi-Agent oberts.
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Hamdi, Sana. "Computational models of trust and reputation in online social networks." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL001/document.
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Les réseaux sociaux ont connu une évolution dramatique et ont été utilisés comme des moyens pour exercer plusieurs activités. En fait, via les réseaux sociaux, les utilisateurs peuvent découvrir, gérer et partager leurs expériences et avis en ligne. Cependant, la nature ouverte et décentralisée des réseaux sociaux les rend vulnérables à l'apparition des utilisateurs malveillants. Par conséquent, les utilisateurs éventuels peuvent faire face à plusieurs de problèmes liés à la confiance. Ainsi, une évaluation de confiance effective et efficace est très importante pour la prise de décisions par ces utilisateurs. En effet, elle leur fournit des informations précieuses leur permettant de faire la différence entre ceux dignes et indignes de confiance. Cette thèse a pour but de fournir des méthodes de gestion de confiance et de réputation des utilisateurs des réseaux sociaux efficaces et qui peuvent être présentées par les quatre contributions suivantes. La première contribution présente une complexe extraction des contextes et des intérêts des utilisateurs, où les informations contextuelles sociales complexes sont prises en compte, reflétant mieux les réseaux sociaux. De plus, nous proposons un enrichissement de l'ontologie Dbpedia par des concepts de folksonomies.Ensuite, nous proposons une approche de gestion de la confiance, intitulée IRIS, permettant la génération du réseau de confiance et le calcul de la confiance directe. Cette approche considère les activités sociales des utilisateurs incluant leurs relations sociales, préférences et interactions.La troisième contribution de cette thèse est la gestion de transitivité de confiance dans les réseaux sociaux. En fait, c'est nécessaire et significatif d'évaluer la confiance entre deux participants n’ayant pas des interactions directes. Nous proposons ainsi, un modèle d'inférence de confiance, appelé TISON, pour évaluer la confiance indirecte dans les réseaux sociaux.La quatrième contribution de cette thèse consiste à gérer la réputation des utilisateurs des réseaux sociaux. Pour ce faire, nous proposons deux nouveaux algorithmes. Nous présentons un nouvel algorithme exclusif pour la classification des utilisateurs basés sur leurs réputations, appelé le RePC. De plus, nous proposons un deuxième algorithme, FCR, qui présente une extension floue de RePC. Pour les approches proposées, nous avons conduits différentes expérimentations sur des ensembles de données réels ou aléatoires. Les résultats expérimentaux ont démontré que nos algorithmes proposés produisent de meilleurs résultats, en termes de qualité des résultats livrés et d’efficacité, par rapport à différentes approches introduites dans littératureOnline Social Networks (OSNs) have known a dramatic increase and they have been used as means for a rich variety of activities. In fact, within OSNs, usersare able to discover, extend, manage, and leverage their experiences and opinionsonline. However, the open and decentralized nature of the OSNs makes themvulnerable to the appearance of malicious users. Therefore, prospective users facemany problems related to trust. Thus, effective and efficient trust evaluation isvery crucial for users’ decision-making. It provides valuable information to OSNsusers, enabling them to make difference between trustworthy and untrustworthyones. This thesis aims to provide effective and efficient trust and reputationmanagement methods to evaluate trust and reputation of OSNs users, which canbe divided into the following four contributions.The first contribution presents a complex trust-oriented users’ contexts andinterests extraction, where the complex social contextual information is taken intoaccount in modelling, better reflecting the social networks in reality. In addition,we propose an enrichment of the Dbpedia ontology from conceptualizations offolksonomies.We second propose the IRIS (Interactions, Relationship types and Interest Similarity)trust management approach allowing the generation of the trust networkand the computation of direct trust. This model considers social activities of usersincluding their social relationships, preferences and interactions. The intentionhere is to form a solid basis for the reputation and indirect trust models.The third contribution of this thesis is trust inference in OSNs. In fact, it isnecessary and significant to evaluate the trust between two participants whomhave not direct interactions. We propose a trust inference model called TISON(Trust Inference in Social Networks) to evaluate Trust Inference within OSNs.The fourth contribution of this thesis consists on the reputation managementin OSNs. To manage reputation, we proposed two new algorithms. We introducea new exclusive algorithm for clustering users based on reputation, called RepC,based on trust network. In addition, we propose a second algorithm, FCR, whichis a fuzzy extension of RepC.For the proposed approaches, extensive experiments have been conducted onreal or random datasets. The experimental results have demonstrated that ourproposed algorithms generate better results, in terms of the utility of delivered results and efficiency, than do the pioneering approaches of the literature
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Sharabati, Walid. "Multi-mode and evolutionary networks." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3384.
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Thesis (Ph.D.)--George Mason University, 2008.Vita: p. 214-215. Thesis director: Edward J. Wegman, Yasmin H. Said Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Title from PDF t.p. (viewed Mar. 9, 2009). Includes bibliographical references (p. 209-213). Also issued in print.
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Noulas, Anastasios. "Human urban mobility in location-based social networks : analysis, models and applications." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648354.
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Corley, Courtney David. "Social Network Simulation and Mining Social Media to Advance Epidemiology." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc11053/.
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Traditional Public Health decision-support can benefit from the Web and social media revolution. This dissertation presents approaches to mining social media benefiting public health epidemiology. Through discovery and analysis of trends in Influenza related blogs, a correlation to Centers for Disease Control and Prevention (CDC) influenza-like-illness patient reporting at sentinel health-care providers is verified. A second approach considers personal beliefs of vaccination in social media. A vaccine for human papillomavirus (HPV) was approved by the Food and Drug Administration in May 2006. The virus is present in nearly all cervical cancers and implicated in many throat and oral cancers. Results from automatic sentiment classification of HPV vaccination beliefs are presented which will enable more accurate prediction of the vaccine's population-level impact. Two epidemic models are introduced that embody the intimate social networks related to HPV transmission. Ultimately, aggregating these methodologies with epidemic and social network modeling facilitate effective development of strategies for targeted interventions.
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Baker, Razan. "Online social networks and Saudi youth participation in physical activity." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/14522.
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Previous studies targeting youth participation in physical activity have argued that self-motivation is the main key to increasing participation. However, few studies have focused specifically on the role of structural factors in prompting youth participation in physical activity. The structure may include people, and institutions that are introducing, providing and facilitating physical activity to youth. Therefore, this study focuses on the role of the structure surrounding youth. The study takes youth in Saudi Arabia aged 15-24 as its subjects in order to examine the use of three online social networks (OSNs), i.e., Facebook, Twitter, and Instagram, for communication and exchange of resources and the influence on participation of key decision makers such as home (parents and siblings), school (Ministry of Education and PE teachers), physical activity and sports clubs (General Authority of Sports [GAS]), and friends. The study uses mixed methods and follows the social network structural theory to examine how the exchange of resources (e.g., information, emotional support, financial support, and facilities and services) takes place between agent and structure. The main findings are that the structure plays a role in influencing participation among Saudi youth. Friends are of great influence, as they occupy the longest hours of youth time both at school, where friends interact in person, and outside of school, where friends communicate through OSNs. An Islamic and conservative society prevails in Saudi Arabia, where 99 per cent of the population is Muslim. Therefore, in addition to the structural factors noted above, religion is also investigated. Indeed, Islam drives motivation in this large conservative group as individuals learn to obey and implement the religious advice and Islamic teachings of the prophet Mohammed (peace be upon him), including those messages with relation to becoming a healthy and strong Muslim. Finally, the study also focuses on the participation of Saudi female youth in physical activity. Due to cultural reasons preventing women from participating in physical activity as freely and equally as their male peers in the country, Saudi Arabia has seen an increasing percentage of obese women. The main aim of this research is to understand the relationship between agency and structure and thereby to identify the role of structure in increasing the participation of youth in physical activity. The research question (How do OSNs facilitate Saudi youth participation in physical activity?) investigates the relationship between agency and structure to delineate the pattern of information exchange regarding resources for involvement in physical activity. Through the use of mixed methods including face-to-face interviews, online survey and digital ethnography, the researcher investigates how youth social networks function both offline and online. The study concludes that decision makers in the field of physical activity participation in Saudi Arabia vary in their level of encouragement, influence and communication. Family members do not seem to communicate with youth via online platforms, but they do play a crucial role in offline social networks. Private institutions are becoming very active in OSNs, and public institutions are following the trend, albeit at a slower pace. The study shows that physical activity facilitators in Saudi Arabia are still failing to effectively reach youth and encourage them to participate in physical activity. Various policies need to be reviewed and enhanced if the public institutions do indeed want to reach more youth and benefit youth and the community, including the female youth, who make up more than half of the population. The study shows that the way to develop these policies is to communicate with youth via OSNs and to provide youth with more facilities, venues and services in the country that are suitable for both genders.
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Doo, Myungcheol. "Spatial and social diffusion of information and influence: models and algorithms." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44740.
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With the ubiquity of broadband, wireless and mobile networking and the diversity of user-driven social networks and social channels, we are entering an information age where people and vehicles are connected at all times, and information and influence are diffused continuously through not only traditional authoritative media such as news papers, TV and radio broadcasting, but also user-driven new channels for disseminating information and diffusing influence. Social network users and mobile travelers can influence and be influenced by the social and spatial connectivity that they share through an impressive array of social and spatial channels, ranging from friendship, activity, professional or social groups to spatial, location-aware, and mobility aware events.
In this dissertation research, we argue that spatial alarms and activity-based social networks are two fundamentally new types of information and influence diffusion channels. Such new channels have the potential of enriching our professional experiences and our personal life quality in many unprecedented ways. For instance, spatial alarms enable people to share their experiences or disseminate certain points of interest by leaving location-dependent greetings, tips or graffiti and location dependent tour guide to their friends, colleagues and family members. Through social networks, people can influence their friends and colleagues by the activities they have engaged, such as reviews and blogs on certain events or products. More interestingly, the power of such spatial and social diffusion of information and influence can go far beyond our physical reach. People can utilize user-generated social and spatial channels as effective means to disseminate information and propagate influence to a much wider and possibly unknown range of audiences and recipients at any time and in any location. A fundamental challenge in embracing such new and exciting ways of information diffusion is to develop effective and scalable models and algorithms as enabling technology and building blocks. This dissertation research is dedicated towards this ultimate objective with three novel and unique contributions.
First, we develop an activity driven and self-configurable social influence model and a suite of computational algorithms to compute and rank social network nodes in terms of activity-based influence diffusion over social network topologies. By activity driven we mean that the real impact of social influence and the speed of such influence propagation should be computed based on the type, the amount and the time window of the activities performed by a social network node in addition to its social connectivity (social network topology). By self-configurable we mean that the diffusion efficiency and effectiveness are dynamically adapted based on the settings and tunings of multiple spatial and social parameters such as diffusion context, diffusion location, diffusion rate, diffusion energy (heat), diffusion coverage and diffusion incentives (e.g., reward points), to name a few. We evaluate our approach through datasets collected from Facebook, Epinions, and DBLP datasets. Our experimental results show that our activity based social influence model outperforms existing topology-based social influence model in terms of effectiveness and quality with respect to influence ranking and influence coverage computation.
Second, we further enhance our activity based social influence model along two dimensions. At first, we use a probabilistic diffusion model to capture the intrinsic properties of social influence such that nodes in a social network may have the choice of whether to participate in a social influence propagation process. We examine threshold based approach and independent probabilistic cascade based approach to determine whether a node is active or inactive in each round of influence diffusion. Secondly, we introduce incentives using multi-scale reward points, which are popularly used in many business settings. We then examine the effectiveness of reward points based incentives in stimulating the diffusion of social influences. We show that given a set of incentives, some active nodes may become more active whereas some inactive nodes may become active. Such dynamics changes the composition of the top-k influential nodes computed by activity-based social influence model. We make several interesting observations: First, popular users who are high degree nodes and have many friends are not necessarily influential in terms of spawning new activities or spreading ideas and information. Second, most influential users are more active in terms of their participation in the social activities and interactions with their friends in the social network. Third, multi-scale reward points based incentives can be effective to both inactive nodes and active nodes.
Third, we introduce spatial alarms as the basic building blocks for location-dependent information sharing and influence diffusion. People can share and disseminate their location based experiences and points of interest to their friends and colleagues in the form of spatial alarms. Spatial alarms are triggered and delivered to the intended subscribers only when the subscribers move into the designated geographical vicinity of the spatial alarms, enabling delivering and sharing of relevant information and experience at the right location and the right time with the right subscribers. We studied how to use locality filters and subscriber filers to enhance the spatial alarm processing using traditional spatial indexing techniques. In addition, we develop a fast spatial alarm indexing structure and algorithms, called Mondrian Tree, and demonstrate that the Mondrian tree enabled spatial alarm system can significantly outperform existing spatial indexing based solutions such as R-tree, $k$-d tree, Quadtree.
This dissertation consists of six chapters. The first chapter introduces the research hypothesis. We describe our activity-based social influence model in Chapter 2. Chapter 3 presents the probabilistic social influence model powered with rewards incentives. We introduce spatial alarms and the basic system architecture for spatial alarm processing in Chapter 4. We describe the design of our Mondrian tree index of spatial alarms and alarm free regions in Chapter 5. In Chapter 6 we conclude the dissertation with a summary of the unique research contributions and a list of open issues closely relevant to the research problems and solution approaches presented in this dissertation.
Books on the topic "Online social networks – Mathematical models"
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Kesidis, George. An introduction to models of online peer-to-peer social networking. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
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Un mondo piccolo ma lento: Dalla fisica ai social network. Ariccia (RM): Aracne editrice int.le S.r.l., 2015.
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1981-, Lin W. Sabrina, and Liu, K. J. Ray, 1961-, eds. Behavior dynamics in media-sharing social networks. Cambridge: Cambridge University Press, 2011.
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Pattison, Philippa. Algebraic models for social networks. Cambridge [England]: Cambridge University Press, 1993.
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1946-, Carrington Peter J., Scott John, and Wasserman Stanley, eds. Models and methods in social network analysis. Cambridge: Cambridge University Press, 2005.
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Dutta, Bhaskar. Networks and groups: Models of strategic formation. Berlin: Springer, 2003.
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Sääskilahti, Pekka. Essays on the economics of networks and social relations. [Helsinki]: Helsinki School of Economics, 2005.
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Leenders, Roger Th A. J. Structure and influence: Statistical models for the dynamics of actor attributes, network structure, and their interdependence. Amsterdam: Thesis Publishers, 1995.
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Boyd, John Paul. Social semigroups: A unified theory of scaling and blockmodelling as applied to social networks. Fairfax, Va: George Mason University Press, 1991.
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Workshop on Dynamic Social Network Modeling and Analysis (2002 Washington, D.C.). Dynamic Social Network Modeling and Analysis: Workshop summary and papers. Washington, D.C: National Academies Press, 2003.
Find full textBook chapters on the topic "Online social networks – Mathematical models"
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Kumar, Ravi, Jasmine Novak, and Andrew Tomkins. "Structure and Evolution of Online Social Networks." In Link Mining: Models, Algorithms, and Applications, 337–57. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6515-8_13.
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Wang, Haiyan, Feng Wang, and Kuai Xu. "Ordinary Differential Equation Models on Social Networks." In Surveys and Tutorials in the Applied Mathematical Sciences, 3–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38852-2_2.
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Harada, Jimpei, David Darmon, Michelle Girvan, and William Rand. "Prediction of Elevated Activity in Online Social Media Using Aggregated and Individualized Models." In Lecture Notes in Social Networks, 169–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53420-6_7.
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Kesidis, George. "Search in structured networks." In An Introduction to Models of Online Peer-to-Peer Social Networking, 41–49. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-79998-3_4.
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Kesidis, George. "Search in unstructured networks." In An Introduction to Models of Online Peer-to-Peer Social Networking, 51–54. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-79998-3_5.
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Hu, Changjun, Wenwen Xu, and Peng Shi. "Information Diffusion in Online Social Networks: Models, Methods and Applications." In Web-Age Information Management, 65–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23531-8_6.
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Salvatori, Roberto. "Advanced Technologies for Social Communication: Methods and Techniques in Online Learning." In Mathematical-Statistical Models and Qualitative Theories for Economic and Social Sciences, 333–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54819-7_22.
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Shiekh, Mohd Abas, Kalpana Sharma, and Aaquib Hussain Ganai. "Information Diffusion: Survey to Models and Approaches, a Way to Capture Online Social Networks." In Intelligent Data Communication Technologies and Internet of Things, 25–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34080-3_3.
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Treur, Jan. "Mathematical Details of Specific Difference and Differential Equations and Mathematical Analysis of Emerging Network Behaviour." In Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models, 375–403. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31445-3_15.
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Röchert, Daniel, German Neubaum, and Stefan Stieglitz. "Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models." In Disinformation in Open Online Media, 107–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_8.
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Abstract Since social media have increasingly become forums to exchange personal opinions, more and more approaches have been suggested to analyze those sentiments automatically. Neural networks and traditional machine learning methods allow individual adaption by training the data, tailoring the algorithm to the particular topic that is discussed. Still, a great number of methodological combinations involving algorithms (e.g., recurrent neural networks (RNN)), techniques (e.g., word2vec), and methods (e.g., Skip-Gram) are possible. This work offers a systematic comparison of sentiment analytical approaches using different word embeddings with RNN architectures and traditional machine learning techniques. Using German comments of controversial political discussions on YouTube, this study uses metrics such as F1-score, precision and recall to compare the quality of performance of different approaches. First results show that deep neural networks outperform multiclass prediction with small datasets in contrast to traditional machine learning models with word embeddings.
Conference papers on the topic "Online social networks – Mathematical models"
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Huang, Donghong, and Chi-Hung Chi. "A mathematical value system model for agent in online social network." In 2011 11th International Conference on Hybrid Intelligent Systems (HIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/his.2011.6122133.
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Chairunnanda, Prima, Simon Forsyth, and Khuzaima Daudjee. "Graph data partition models for online social networks." In the 23rd ACM conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2309996.2310026.
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Balagura, Kyrill, Helen Kazakova, Daliant Maximus, and Victoria Turygina. "Mathematical models of cognitive interaction identification in the social networks." In CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5114453.
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Jiang, Wenjun, and Jie Wu. "Trust Models in Wireless Sensor Networks and Online Social Networks: A Comparative Study." In 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2014. http://dx.doi.org/10.1109/mass.2014.71.
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Cramer, Marcos, Jun Pang, and Yang Zhang. "A Logical Approach to Restricting Access in Online Social Networks." In SACMAT '15: 20th ACM Symposium on Access Control Models and Technologies. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2752952.2752967.
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Barbosa Neto, Samuel Martins, Maira Athanazio De Cerqueira Gatti, Paulo Rodrigo Cavalin, Claudio Santos Pinhanez, Cicero Nogueira Dos Santos, and Ana Paula Appel. "Reaction times for user behavior models in microblogging online social networks." In the 2103 workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2513577.2513578.
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Lee, Duan-Shin, Cheng-Shang Chang, Wen-Gui Ye, and Min-Chien Cheng. "Analysis of clustering coefficients of online social networks by duplication models." In ICC 2014 - 2014 IEEE International Conference on Communications. IEEE, 2014. http://dx.doi.org/10.1109/icc.2014.6883962.
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Ni, Minyue, Yang Zhang, Weili Han, and Jun Pang. "An Empirical Study on User Access Control in Online Social Networks." In SACMAT 2016: The 21st ACM Symposium on Access Control Models and Technologies. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2914642.2914644.
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Sathanur, Arun V., Vikram Jandhyala, and Chuanjia Xing. "PHYSENSE: Scalable sociological interaction models for influence estimation on online social networks." In 2013 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2013. http://dx.doi.org/10.1109/isi.2013.6578858.
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Lovato, Juniper L., Antoine Allard, Randall Harp, Jeremiah Onaolapo, and Laurent Hébert-Dufresne. "Limits of Individual Consent and Models of Distributed Consent in Online Social Networks." In FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531146.3534640.
Full textReports on the topic "Online social networks – Mathematical models"
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McKenna, Patrick, and Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, June 2021. http://dx.doi.org/10.5204/rep.eprints.211133.
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Emergency Relief (ER) is a Department of Social Services (DSS) funded program, delivered by 197 community organisations (ER Providers) across Australia, to assist people facing a financial crisis with financial/material aid and referrals to other support programs. ER has been playing this important role in Australian communities since 1979. Without ER, more people living in Australia who experience a financial crisis might face further harm such as crippling debt or homelessness. The Emergency Relief National Coordination Group (NCG) was established in April 2020 at the start of the COVID-19 pandemic to advise the Minister for Families and Social Services on the implementation of ER. To inform its advice to the Minister, the NCG partnered with the Institute for Governance at the University of Canberra to conduct research to understand the issues and challenges faced by ER Providers and Service Users in local contexts across Australia. The research involved a desktop review of the existing literature on ER service provision, a large survey which all Commonwealth ER Providers were invited to participate in (and 122 responses were received), interviews with a purposive sample of 18 ER Providers, and the development of a program logic and theory of change for the Commonwealth ER program to assess progress. The surveys and interviews focussed on ER Provider perceptions of the strengths, weaknesses, future challenges, and areas of improvement for current ER provision. The trend of increasing case complexity, the effectiveness of ER service delivery models in achieving outcomes for Service Users, and the significance of volunteering in the sector were investigated. Separately, an evaluation of the performance of the NCG was conducted and a summary of the evaluation is provided as an appendix to this report. Several themes emerged from the review of the existing literature such as service delivery shortcomings in dealing with case complexity, the effectiveness of case management, and repeat requests for service. Interviews with ER workers and Service Users found that an uplift in workforce capability was required to deal with increasing case complexity, leading to recommendations for more training and service standards. Several service evaluations found that ER delivered with case management led to high Service User satisfaction, played an integral role in transforming the lives of people with complex needs, and lowered repeat requests for service. A large longitudinal quantitative study revealed that more time spent with participants substantially decreased the number of repeat requests for service; and, given that repeat requests for service can be an indicator of entrenched poverty, not accessing further services is likely to suggest improvement. The interviews identified the main strengths of ER to be the rapid response and flexible use of funds to stabilise crisis situations and connect people to other supports through strong local networks. Service Users trusted the system because of these strengths, and ER was often an access point to holistic support. There were three main weaknesses identified. First, funding contracts were too short and did not cover the full costs of the program—in particular, case management for complex cases. Second, many Service Users were dependent on ER which was inconsistent with the definition and intent of the program. Third, there was inconsistency in the level of service received by Service Users in different geographic locations. These weaknesses can be improved upon with a joined-up approach featuring co-design and collaborative governance, leading to the successful commissioning of social services. The survey confirmed that volunteers were significant for ER, making up 92% of all workers and 51% of all hours worked in respondent ER programs. Of the 122 respondents, volunteers amounted to 554 full-time equivalents, a contribution valued at $39.4 million. In total there were 8,316 volunteers working in the 122 respondent ER programs. The sector can support and upskill these volunteers (and employees in addition) by developing scalable training solutions such as online training modules, updating ER service standards, and engaging in collaborative learning arrangements where large and small ER Providers share resources. More engagement with peak bodies such as Volunteering Australia might also assist the sector to improve the focus on volunteer engagement. Integrated services achieve better outcomes for complex ER cases—97% of survey respondents either agreed or strongly agreed this was the case. The research identified the dimensions of service integration most relevant to ER Providers to be case management, referrals, the breadth of services offered internally, co-location with interrelated service providers, an established network of support, workforce capability, and Service User engagement. Providers can individually focus on increasing the level of service integration for their ER program to improve their ability to deal with complex cases, which are clearly on the rise. At the system level, a more joined-up approach can also improve service integration across Australia. The key dimensions of this finding are discussed next in more detail. Case management is key for achieving Service User outcomes for complex cases—89% of survey respondents either agreed or strongly agreed this was the case. Interviewees most frequently said they would provide more case management if they could change their service model. Case management allows for more time spent with the Service User, follow up with referral partners, and a higher level of expertise in service delivery to support complex cases. Of course, it is a costly model and not currently funded for all Service Users through ER. Where case management is not available as part of ER, it might be available through a related service that is part of a network of support. Where possible, ER Providers should facilitate access to case management for Service Users who would benefit. At a system level, ER models with a greater component of case management could be implemented as test cases. Referral systems are also key for achieving Service User outcomes, which is reflected in the ER Program Logic presented on page 31. The survey and interview data show that referrals within an integrated service (internal) or in a service hub (co-located) are most effective. Where this is not possible, warm referrals within a trusted network of support are more effective than cold referrals leading to higher take-up and beneficial Service User outcomes. However, cold referrals are most common, pointing to a weakness in ER referral systems. This is because ER Providers do not operate or co-locate with interrelated services in many cases, nor do they have the case management capacity to provide warm referrals in many other cases. For mental illness support, which interviewees identified as one of the most difficult issues to deal with, ER Providers offer an integrated service only 23% of the time, warm referrals 34% of the time, and cold referrals 43% of the time. A focus on referral systems at the individual ER Provider level, and system level through a joined-up approach, might lead to better outcomes for Service Users. The program logic and theory of change for ER have been documented with input from the research findings and included in Section 4.3 on page 31. These show that ER helps people facing a financial crisis to meet their immediate needs, avoid further harm, and access a path to recovery. The research demonstrates that ER is fundamental to supporting vulnerable people in Australia and should therefore continue to be funded by government.