Social Network Model Research Articles

A semi-automated forensic investigation model for online social networks

Investigating the online social network profiles of victims, suspects, and witnesses are now part of almost every legal investigation, either it involves a criminal offense, financial fraud, or domestic lawsuit. However, investigating online social networks (OSN) is a technically complicated process that becomes more challenging due to the legal issues of privacy and authentication. Completely manual investigative methods are not feasible for OSN investigations due to the immense size and heterogeneity of social networks. However, the existing models for digital forensic investigation are not supporting automated or semi-automated forensic investigation processes. Furthermore, they are not addressing the fundamental differences and specific requirements of online social networks. The model presented in this work incorporates the robust features of standard investigation models and proposes a digital forensic investigation process model that explicitly addresses the necessities of OSN investigations. This work is addressing the issues of automating the forensic collection and analysis processes, defining crime scene boundaries, and outlining reasonable iterative collection procedures in online social network forensic investigation. This work is evaluated using a case study and is compared with existing practices and standards.

Link Weight Prediction Using Weight Perturbation and Latent Factor.

Link weight prediction is an important subject in network science and machine learning. Its applications to social network analysis, network modeling, and bioinformatics are ubiquitous. Although this subject has attracted considerable attention recently, the performance and interpretability of existing prediction models have not been well balanced. This article focuses on an unsupervised mixed strategy for link weight prediction. Here, the target attribute is the link weight, which represents the correlation or strength of the interaction between a pair of nodes. The input of the model is the weighted adjacency matrix without any preprocessing, as widely adopted in the existing models. Extensive observations on a large number of networks show that the new scheme is competitive to the state-of-the-art algorithms concerning both root-mean-square error and Pearson correlation coefficient metrics. Analytic and simulation results suggest that combining the weight consistency of the network and the link weight-associated latent factors of the nodes is a very effective way to solve the link weight prediction problem.

Social influence or selection? Peer effects on the development of adolescents’ educational expectations in Germany

Educational expectations are a key predictor of educational attainment. Throughout adolescence, friends increasingly function as ‘significant others’ and, thus, can affect the development of these expectations. Although scholars often interpret the clustering of students with similar expectations within friendship networks as the outcome of peer influence, a similarity of friends can also be a result of friendship selection processes and preselection due to ability tracking. We apply multilevel social network models to panel data of adolescents from Germany (1,992 ninth-grade students in 91 classes) to disentangle these mechanisms. Beyond selecting similar friends (homophily), we find that adolescents adapt their expectations towards the average expectations of their friends (social influence) but only in secondary-school tracks that support diverse educational paths. We conclude that peer socialization is important for the development of students’ educational expectations in contexts that are sufficiently heterogeneous to allow for the emergence of distinct peer milieus.

La Sociolingüística de Redes y los Social Media

In this paper, the model of Social Networks –proposed in 1987 by J. Milroy and L. Milroy– is applied to the analysis of linguistic interactions that happen in Facebook and Twitter, two social networks with a similar purpose but they behave in a different way and they provide different types of reticulated structures. The linguistic behavior of users of social media is studied in order to determine whether the general principles defensed in the reticular studies applied to the linguistic variation are achieved in the language used in digital platforms. Our results show that there is a big contrast between the linguistic characteristics that present the people who are part of a dense and multiple network –Facebook– and the ones that take part in a relaxed and simple network –Twitter.

Sign prediction by motif naive Bayes model in social networks

Sign prediction is a significant research content in signed social networks, so it has attracted increasing attention from the field of online social networks recently. Traditionally, the basic idea of motif-based predictive method is to calculate the motif number on the predicted edge (i.e., the single edge-dependent motif based method), and then use a machine learning predictor for sign prediction. Although this intuition-based method can achieve great performance for sign prediction, up to now its reasonability has not been proved theoretically. Furthermore, the method of counting the number of edge-dependent motif can not distinguish the distinct role of each node on the neighborhood of the predicted edge. In this study, firstly we propose a Single Motif Naive Bayes (SMNB) model for sign prediction, which can not only explain why the single edge-dependent motif based method is efficient, but also quantify the role of each neighbor node (for 3-node predictors) or neighbor edge (for 4-node predictors) which is connected by the predicted edge for the task of sign prediction. Then, we extend SMNB by merging two types of motifs, and propose a Two Motif Naive Bayes (TMNB) model. Experimental results on real-world networks indicate that the proposed algorithms outperform the state-of-the-art approaches. Finally, we explore the intrinsic relationship among different motifs according to the matrix of Maximal Information Coefficient (MIC). Our research not only extends the traditional motif theory by proving the rationality of the edge-dependent motif based method and distinguishing a node (or an edge) contribution for sign prediction, but also is helpful to further understand the evolution mechanism of signed social networks based on the correlation among different motifs.

Structural centrality in fuzzy social networks based on fuzzy hypergraph theory

The knowledge of key network members is generally known to be critical to fuzzy social network analysis. Thus far, most studies aiming to identify critical members have taken network structural centrality measures. Since fuzzy graph cannot effectively depict the multidimensional relationships between the nodes of fuzzy social networks, a fuzzy social network model is developed complying with a mathematical theory of fuzzy hypergraph, allowing fuzzy social network to be represented more intuitively and visually. A fuzzy hypergraph model of fuzzy social network refers to a structure, vertex set acts as an object set, and the fuzzy relation in fuzzy relation structure is expressed by membership function and fuzzy relation matrix. With the fuzzy hypergraph model of fuzzy social networks, the definitions of structural centrality are given (i.e., degree centrality, relative degree centrality, closeness centrality, relative closeness centrality, betweenness centrality and relative betweenness centrality). Lastly, by analyzing examples, the process of building fuzzy social network with fuzzy hypergraph and the calculation method of centrality are illustrated.

Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world.

Social distancing and isolation have been widely introduced to counter the COVID-19 pandemic. Adverse social, psychological and economic consequences of a complete or near-complete lockdown demand the development of more moderate contact-reduction policies. Adopting a social network approach, we evaluate the effectiveness of three distancing strategies designed to keep the curve flat and aid compliance in a post-lockdown world. These are: limiting interaction to a few repeated contacts akin to forming social bubbles; seeking similarity across contacts; and strengthening communities via triadic strategies. We simulate stochastic infection curves incorporating core elements from infection models, ideal-type social network models and statistical relational event models. We demonstrate that a strategic social network-based reduction of contact strongly enhances the effectiveness of social distancing measures while keeping risks lower. We provide scientific evidence for effective social distancing that can be applied in public health messaging and that can mitigate negative consequences of social isolation.

Analysis and visualisation of stakeholder relationship in Offsite construction: Social Network Analysis approach

While Off-site construction has emerged recently, its benefits have long been proved worldwide. Many hold that Off-site construction is re-structuring the industry, its supply chain and setting supplementary standards. Although stakeholder theory is prevailing in project management research, there is still a lack of in-depth research upon patterns of interrelationship and communication among Off-site construction stakeholders. Given that in Off-site construction project-based organisations are multi-level structures, stakeholders interact with each other not only within-level but also cross-level. To facilitate the adoption and diffusion of Off-site construction, there is a need to address patterns and find out the optimal mechanism for promoting inter-collaboration. Social Network Analysis is presented as a holistic approach to meet that need in a more quantitative and unambiguous way. Based on a critique of Off-site construction literature and Social Network Analysis theory, a conceptual framework is also proposed for Off-site construction stakeholder research themes and corresponding methodology. The aim of this paper is to contribute to a sound theoretical foundation for the development of a social network model and the understanding of the complex of connection and relationship among Off-site construction stakeholders.

The iterated local model for social networks

On-line social networks, such as in Facebook and Twitter, are often studied from the perspective of friendship ties between agents in the network. Adversarial ties, however, also play an important role in the structure and function of social networks, but are often hidden. Underlying generative mechanisms of social networks are predicted by structural balance theory, which postulates that triads of agents, prefer to be transitive, where friends of friends are more likely friends, or anti-transitive, where adversaries of adversaries become friends. The previously proposed Iterated Local Transitivity (ILT) and Iterated Local Anti-Transitivity (ILAT) models incorporated transitivity and anti-transitivity, respectively, as evolutionary mechanisms. These models resulted in graphs with many observable properties of social networks, such as low diameter, high clustering, and densification.We propose a new, generative model, referred to as the Iterated Local Model (ILM) for social networks synthesizing both transitive and anti-transitive triads over time. In ILM, we are given a countably infinite binary sequence as input, and that sequence determines whether we apply a transitive or an anti-transitive step. The resulting model exhibits many properties of complex networks observed in the ILT and ILAT models. In particular, for any input binary sequence, we show that asymptotically the model generates finite graphs that densify, have clustering coefficient bounded away from 0, have diameter at most 3, and exhibit bad spectral expansion. We also give a thorough analysis of the chromatic number, domination number, Hamiltonicity, and isomorphism types of induced subgraphs of ILM graphs.

A Discrete Mathematical Modeling and Optimal Control of the Rumor Propagation in Online Social Network

In this paper, a new rumor spreading model in social networks has been investigated. We propose a new version primarily based on the cholera model in order to take into account the expert pages specialized in the dissemination of rumors from an existing IRCSS model. In the second part, we recommend an optimal control strategy to fight against the spread of the rumor, and the study aims at characterizing the three optimal controls which minimize the number of spreader users, fake pages, and corresponding costs; theoretically, we have proved the existence of optimal controls, and we have given a characterization of controls in terms of states and adjoint functions based on a discrete version of Pontryagin’s maximum principle. To illustrate the theoretical results obtained, we propose numerical simulations for several scenarios applying the forward-backward sweep method (FBSM) to solve our optimality system in an iterative process.

Climate Change and Migration: New Insights from a Dynamic Model of Out-Migration and Return Migration.

In popular accounts, stories of environmental refugees convey a bleak picture of the impacts of climate change on migration. Scholarly research is less conclusive, with studies finding varying effects. This paper uses an agent-based model (ABM) of land use, social networks, and household dynamics to examine how extreme floods and droughts affect migration in Northeast Thailand. The ABM explicitly models the dynamic and interactive pathways through which climate-migration relationships might operate, including coupled out and return streams. Results suggest minimal effects on out-migration but marked negative effects on return. Social networks play a pivotal role in producing these patterns. In all, the portrait of climate change and migration painted by focusing only on environmental refugees is too simple. Climate change operates on already established migration processes that are part and parcel of the life course, embedded in dynamic social networks, and incorporated in larger interactive systems where out- and return migration are integrally connected.

A Social Network Model of COVID-19

I construct a dynamic social-network based model of the COVID-19 epidemic which embeds the SIR epidemiological model onto a graph of person-to-person interactions. The standard SIR framework assumes uniform mixing of infectious persons in the population. This abstracts from important elements of realism and locality: (i) people are more likely to interact with members of their social networks and (ii) health and economic policies can affect differentially the rate of viral transmission via a person's social network vs. the population as a whole. The proposed network-augmented (NSIR) model allows the evaluation, via simulations, of (i) health and economic policies and outcomes for all or subset of the population: herd immunity, testing, contact tracing, lockdown/distancing; (ii) behavioral responses and/or imposing or lifting policies at a specific time or conditional on observed states. As the NSIR model keeps track of individual states, an economic cost-benefit module and agent heterogeneity (e.g., in savings, employment status; ability to pay bills) is easily incorporated. I find that viral transmission over a network-connected population can proceed slower and reach lower peak than transmission via uniform contacts. The resulting longer epidemic duration may imply larger overall economic costs, e.g., if accompanied by prolonged lockdown policies. If lifted early, distancing policies mostly shift the infection peak into the future with associated economic costs. Delayed or intermittent (on-off-on) interventions or endogenous behavioral responses can lead to a twin-peaked infection rate, a form of 'curve flattening', but may have costlier economic consequences by prolonging the epidemic duration.

A two-layer social network model for manufacturing service composition based on synergy: A case study on an aircraft structural part

Abstract Cloud manufacturing is an emerging manufacturing paradigm, with the issue of service composition being one of its most significant challenges. The current researches take the view that all manufacturing services are of equal importance. However, in fact, manufacturing services that owns scarce resources are more important than ordinary one, especially in the production of complex products. Hence, it is significant to meet the requirements of manufacturing services that own scarce resources first. Meanwhile, due to the increasing complexity of products, a high degree of synergy is required in service composition. Our study explores a new framework of social relationships from the perspective of synergy. A two-layer social network model is first proposed for resource allocation that takes synergy and priority into account. The upper layer of this model is occupied by the manufacturing services with scare resources. The lower layer is occupied by ordinary manufacturing services. All services are connected by social relationships. For the master-slave characteristic of the model, the two-layer network is embedded into bi-level programming. Then, an improved genetic algorithm is designed to solve the problem. Finally, the effectiveness of the model and algorithm are verified using a structural part of the Modern Ark 60 as an example.

Analyzing Actors’ Engagement in Sustainable Energy Planning at the Local Level in Ghana: An Empirical Study

Actors play a crucial role in sustainable energy development yet interaction in different contexts is an area that has not received much scholarly attention. Sustainable energy transitions theories such as the Multi-Level Perspective, for instance, have been criticized for not describing precisely the nature of the interactions between actors and institutions within socio-technical systems. The goal of this study was to empirically examine local actors’ engagement and its impact on the planning and implementation of sustainable energy initiatives in the villages and remote areas in Ghana. Using the mixed methodology approach, interviews were performed, focus discussion groups were held, and archival data were collected, and social network modeling and case study analysis was performed. Our findings showed that sustainable energy development at the local level depends on an interplay between local government agencies, Non-Governmental Organizations (NGOs), central government agencies, local communities, and private sector organizations. Despite being the focal point at the local level, local government involvement in sustainable energy planning is limited. In the case of Ghana, sustainable energy planning remains centralized and is manifested in a low level of awareness of local actors on national energy plans. The implication for decision makers is that energy planning functions should be devolved to the local government. Such devolution is expected to ensure the integration of sustainable energies into local government plans for the well-coordinated implementation and effective monitoring of sustainable energy projects.

Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest

Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter) , we try to unveil the correlation between users' sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the influence strength between linked users. Furthermore, we introduce user interest to refine the influence strength. We develop a unified probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users' sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model significantly outperforms several alternative methods in predicting users' sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.

Henneberg Growth of Social Networks: Modeling the Facebook

Social networks are complex in their forming and growing processes. Tremendous empirical evidence in undirected social networks, such as Facebook, Quora and Foursquare, demonstrates that, to a large extent, individuals are associated with each other not by preference but through other organizing rules. One such rule found in many real social networks is the Henneberg growth mechanism, with which a triangle will be formed whenever an individual joins a community. Inspired by this mechanism, a novel social network model, named Henneberg growth model, is proposed in this paper. Some topological and dynamical properties of the model in common interest are analyzed. Experimental results show that the function and structure of the model are in remarkable agreement with two huge-scale Facebook network datasets. The finding suggests that the Henneberg growth mechanism is indeed fundamental for modeling some undirected social networks like the Facebook.

The social network model on infinite graphs

Given an infinite connected regular graph $G=(V,E)$, place at each vertex $\operatorname{Poisson}(\lambda)$ walkers performing independent lazy simple random walks on $G$ simultaneously. When two walkers visit the same vertex at the same time they are declared to be acquainted. We show that when $G$ is vertex-transitive and amenable, for all $\lambda>0$ a.s. any pair of walkers will eventually have a path of acquaintances between them. In contrast, we show that when $G$ is nonamenable (not necessarily transitive) there is always a phase transition at some $\lambda_{\mathrm{c}}(G)>0$. We give general bounds on $\lambda_{\mathrm{c}}(G)$ and study the case that $G$ is the $d$-regular tree in more detail. Finally, we show that in the nonamenable setup, for every $\lambda$ there exists a finite time $t_{\lambda}(G)$ such that a.s. there exists an infinite set of walkers having a path of acquaintances between them by time $t_{\lambda}(G)$.

Knowledge-sharing rewards in enterprise social networks: effects of learner types and impact of digitisation

ABSTRACTThe era of total digitisation demands companies to seek innovative approaches in managing digital assets. This paper presents and studies an analytical model of an enterprise social network (ESN) employed by organisations in facilitating knowledge sharing and learning. Exploring the design of reward systems that motivate workers to share knowledge, we focus on how the dominant type of learners (avid or impressionable), the network structure of the ESN, and digitisation affect the reward systems. This research provides valuable insights for practitioners to implement appropriate reward systems to promote knowledge sharing and learning in ESNs.

Social Networks and Potential Competition Issues

In addition to search engines, social networks represent an important digital service for many Internet users. Social network services such as Facebook, Xing, or Twitter provide vital platforms for communication and sharing of content as part of a modern, user-friendly Internet. Public criticism of social networks is expressed in particular with regard to processing of personal data. These play a central role in business models of many social networks, with regard to their use e.g. for advertising purposes. A question is, if it can be assumed that market-dominant providers, due to a lock-in effect, can demand a wide-ranging consent for the collection and use of personal data that would be expected in a functioning competition? In the following, social networks, potential competition issues and possible legislative measures due to concentration tendencies in connection with access to user data in the area of social networks will be discussed.

Public Goods Games on Coevolving Social Network Models

Public good games are a metaphor for modeling cooperative behavior in groups in the presence of incentives to free ride. In the model presented here agents play a public good game with their neighbors in a social network structure. Agents' decision rules in our model are inspired by elementary learning observed in laboratory and online behavioral experiments involving human participants with the same amount of information, i.e., when individuals only know their own current contribution and their own cumulated payoff. In addition, agents in the model are allowed to severe links with groups in which their payoff is lower and create links to a new randomly chosen public goods game. Reinforcing the results obtained in network scenarios where agents play Prisoner's Dilemma games, we show that thanks to this relinking possibility, the whole system reaches higher levels of average contribution with respect to the case in which the network cannot change. Our setup opens new frameworks to be investigated, and potentially confirmed, through controlled human experiments.