Typical Social Network Research Articles

The Application of Social Networks in the Marketing

Consumption always acts as one of the most significant links to economics. In recent years, the way of consumption has changed entirely due to the evolution of the internet. A dramatic increase in the number of people who tends to obtain product information online instead of offline. So, is this either an opportunity or a disaster for the business? How should enterprises utilise this new tool in this unprecedented reform? In this article, the author will reveal the following difficulties 1. How the Social Networking change consumers behaviour? 2. What are the differences between some typical social network approaches used for diffusion? 3. How to employ the social network in marketing? By analysing the research of previous essays and the several views of the author. Furthermore, it is to identify to them when is the best chance to use which method and what is the most appropriate to the companies employing different approaches to operate.

Social Trust-Driven Consensus Reaching Model for Multiattribute Group Decision Making: Exploring Social Trust Network Completeness

With the development of social media, social networks (SNs) have gradually become the link of information exchange between people. The social trust network (STN), a typical SN, is considered the basis of interaction for experts. Therefore, in the STN environment, an STN-driven consensus decision framework of multi-attribute group decision making (MAGDM) is developed. First, a two-stage analysis method of STN completeness considering the social psychology of experts is proposed to obtain a complete STN, which includes the processes of trust propagation and aggregation. Second, based on the complete modified STN, an STN-driven consensus-reaching process (CRP) is presented with a consensus optimization model aiming at minimum adjustments. In addition, a numerical example is used to further elaborate on the applicability of these methods. Finally, a series of simulation experiments are designed to objectively determine the parameters in the proposed methods and highlight the rationality and superiority of the proposed methods by comparisons and discussions with related studies.

Understanding information diffusion with psychological field dynamic

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.

Multi-mode Network Analysis under Differential Privacy

With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today’s complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to “disassociate” personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end.

Discrete Interactions in Decentralized Multiagent Coordination: A Probabilistic Perspective

While current work has proved that the interactions between tightly coupled multiagent is one of the effective means to overcome the information partially observable constraints, creating coordinative policies among loose coupled agents under uncertainty is still a big challenge. In this paper, we explored how the discrete interactions affect the decentralized multiagent coordination, in which the coordinative relationship changes dynamically with randomly arrived tasks. We first proposed a decision model, DDI-MDPs, which supports both the independence and the interactive decision-making, that is, interactions are spatio-temporal discrete occurred. On this basis, we contributed a heuristic imprecise probabilistic based interaction algorithm, HDLI, which utilizes the multidimensional semantic relevance among agents, information and tasks, so that agents can continuously improve their cognition and optimize the decision-making efficiency by learning the interaction records. In addition, we dissected the transformation conditions between DDI-MDPs and the classic models, and modeled the dynamic relationships into a dynamic graph, and then analyzed its evolution processes. In the simulation, we evaluated the efficiency of the proposed work in several typical coordination scenarios, the results reveal that the interactive-growth relationship between agents has typical social network characteristics. Finally, some possible challenges in the follow-up research work and applications are discussed.

Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles

The latest manifestation of “all connected world" is the Internet of Things (IoT), and Internet of Vehicles (IoV) is one of the key examples of IoT these days. In Social IoV (SIoV), each vehicle is treated as a social object where it establishes and manages its own Social Network (SN). Incidentally, most of the SIoV research in the literature is related to proximity-based connectivity and interactions. In this paper, we bring people in the loop by incorporating their SNs. While emphasizing a recommendation scenario, in which vehicles may require recommendations from SNs of their owners (in addition to their own SIoV), we proposed an agent-based model of information sharing (for context-based recommendations) on a hypothetical population of smart vehicles. Some important hypotheses were tested using a realistic simulation setting. The simulation results reveal that a recommendation using weak ties is more valuable than a recommendation using strong ties in pure SIoV. The simulation results also demonstrate that recommendations using the most-connected person in the social network are not more valuable than recommendation using a random person in the social network. The model presented in this paper can be used to design a multi-scale recommendation system, which uses SIoV and a typical SN in combination.

Modeling Spread of Preferences in Social Networks for Sampling-Based Preference Aggregation

Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population. We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes. We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network. We hence propose an appropriate objective function for the problem of selecting best representative nodes. We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice. We compare the performance of these proposed algorithms against random-polling and popular centrality measures, and provide a detailed analysis of the obtained results. Our analysis suggests that selecting representatives using social network information is advantageous for aggregating preferences related to personal topics (e.g., lifestyle), while random polling with a reasonable sample size is good enough for aggregating preferences related to social topics (e.g., government policies).

Usage of graph databases for social graph modeling

This article is devoted to graph database management systems. The main characteristics and capabilities of those systems have been contemplated. The problems that may occur during the social network development have been selected to be solved using a graph data model. The most popular database management systems nowadays, namely, Neo4J, OrientDB and ArangoDB have been chosen for the study. Such characteristics of the selected databases as whether the software is proprietary or freely distributed, whether databases have up-to-date documentation or not, whether they are supported by developers, whether there is a community where you can get answers to your questions, and how much time is needed to master the database have been elaborated. The typical social network queries, when you need to receive results with a large depth of search quickly, have been developed using the query languages Cypher, OrientDB SQL and AQL used in Neo4J, OrientDB and ArangoDB respectively. The comparison of query execution speed has been performed for the selected databases. For this purpose, a graph that has 5000 nodes and 24900 connections has been built by implementing the Barabashi-Albert model for generating random-scale networks. The test tasks for finding friends of three users with the depth of 5 have been generated. The average time for each request has been estimated for several executions. The conclusions have been drawn and the recommendations regarding the selection of the best graph database for social network implementation have been made.

Understanding structure-based social network de-anonymization techniques via empirical analysis

The rapid development of wellness smart devices and apps, such as Fitbit Coach and FitnessGenes, has triggered a wave of interaction on social networks. People communicate with and follow each other based on their wellness activities. Though such IoT devices and data provide a good motivation, they also expose users to threats due to the privacy leakage of social networks. Anonymization techniques are widely adopted to protect users’ privacy during social data publishing and sharing. However, de-anonymization techniques are actively studied to identify weaknesses in current social network data-publishing mechanisms. In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. We aim to understand the de-anonymization approaches and disclose the impacts on their application performance caused by different factors, e.g., topology properties and anonymization methods adopted to sanitize original data. We design the analysis framework and define three experiment environments to evaluate a few factors’ impacts on the target algorithms. Based on our analysis architecture, we simulate three typical de-anonymization algorithms and evaluate their performance under different pre-configured environments.

Topology, robustness, and structural controllability of the Brazilian Federal Police criminal intelligence network

Law enforcement and intelligence agencies worldwide struggle to find effective ways to fight organized crime and reduce criminality. However, illegal networks operate outside the law and much of the data collected is classified. Therefore, little is known about the structure, topological weaknesses, and control of criminal networks. We fill this gap by presenting a unique criminal intelligence network built directly by the Brazilian Federal Police for intelligence and investigative purposes. We study its structure, its response to different attack strategies, and its structural controllability. Surprisingly, the network composed of individuals involved in multiple crimes of federal jurisdiction in Brazil has a giant component enclosing more than half of all its edges. We focus on the largest connected cluster of this network and show it has many social network features, such as small-worldness and heavy-tail degree distribution. However, it is less dense and less efficient than typical social networks. The giant component also shows a high degree cutoff that is associated with the lack of trust among individuals belonging to clandestine networks. The giant component of the network is also highly modular (Q=0.96) and thence fragile to module-based attacks. The targets in such attacks, i.e. the nodes connecting distinct communities, may be interpreted as individuals with bridging clandestine activities such as accountants, lawyers, or money changers. The network can be disrupted by the removal of approximately 2% of either its nodes or edges, the negligible difference between both approaches being due to low graph density. Finally, we show that 20% of driver nodes can control dynamic variables acting on the whole network, suggesting that non-repressive strategies such as access to basic education or sanitation can be effective in reducing criminality by changing the perception of driver individuals to norm compliance.

Mutual assent or unilateral nomination? A performance comparison of intersection and union rules for integrating self-reports of social relationships

Data collection designs for social network studies frequently involve asking both parties to a potential relationship to report on the presence of absence of that relationship, resulting in two measurements per potential tie. When inferring the underlying network, is it better to estimate the tie as present only when both parties report it as present or do so when either reports it? Employing several data sets in which network structure can be well-determined from large numbers of informant reports, we examine the performance of these two simple rules. Our analysis shows better results for mutual assent across all data sets examined. A theoretical analysis of estimator performance shows that the best rule depends on both underlying error rates and the sparsity of the underlying network, with sparsity driving the superiority of mutual assent in typical social network settings.

Triadic co-clustering of users, issues and sentiments in political tweets

Social network data contains many hidden relationships. The most well known is the communities formed by users. Moreover, typical social network data, such as Twitter, can also be interpreted in terms of three-dimensional relationships; namely the users, issues discussed by the users, and terminology chosen by the users in these discussions. In this paper, we propose a new problem to generate co-clusters in these three dimensions simultaneously. There are three major differences between our problem and the standard co-clustering problem definition: a node can be a member of more than one clusters; all the nodes are not necessarily members of some cluster; and edges are signed and cluster are expected to have high density of positive signed edges, and low density of negative signed edges. We apply our method to the tweets of British politicians just before the Brexit referendum. Our motivation is to discover clusters of politicians, issues and the sentimental words politicians use to express their feelings on these issues in their tweets.

A Novel E-Mail Network Evolution Model based on user Information

E-mail is one of the main means of communication in society today, and it is a typical social network. Studying the evolution of the social network structure by constructing an e-mail network evolution model is of great significance to the literature. In this paper, we first analyze the e-mail network by constructing an e-mail network communication model; this mainly includes analysis of the structure of the e-mail network and analysis of the user information in the e-mail network; then, we propose an e-mail network evolution model based on the characteristics of user information and give the specific evolutionary steps; finally, the simulation experiments are carried out to analyze the characteristics of the model. Experiments show that the nodes are characterized by a power-law distribution, and compared with other models; the model is closer to the real network, so it has important practical significance.

Research on Social Marketing Strategies with An Agent-based Propagation Model

Considering the increasing complexity of social networks and user behaviors, it is very challenging for advertisers to formulate their strategies of selecting proper initial seed users in their social marketing efforts. In this paper, we tackle this challenge by proposing an agent-based propagation model and injecting it into typical social networks with three types of structures, i.e., Erdős-Rényi random graph, Watt-Strogatz small world graph, and Barabási-Albert scale-free graph. We instantiate this agent-based model with demographic characteristics extracted from real-world census data collected in China. By investigating the diffusion process of advertising information in these social networks, we can analyze and compare the performance of advertisers’ targeting and influencer strategies. Our experimental results indicate that advertisers adopting influencer strategies should manipulate the initial well-connected seeds to deliver information only to the potential customers instead of a wide range of generic users.

Social Networks among Ministry Relationships: Relational Capacity, Burnout, & Ministry Effectiveness

Humans, on average, are believed to have the capacity to sustain approximately 150 personal relationships due to social-cognitive limits and time available for relationship investment (Dunbar, 1993). The consequences of attempting to exceed this relational limit have not been investigated. Yet relational-style ministry workers face pressure to increase their number of personal relationships. It is likely that attempting to exceed this relational limit leads to distress. Therefore, relational ministers exceeding typical social network sizes were predicted to experience higher levels of burnout and lower levels of ministry effectiveness. For this study, two hundred thirty-seven relational ministers completed self-report measures. Multiple hierarchical regressions indicated that while total network size was not a significant predictor of outcome variables, nuanced differences among networks predicted burnout and ministry effectiveness. Above average numbers of intimate, high-investment relationships predicted smaller overall network sizes, and subgroups of more intimate relationships may have optimal size ranges that contribute to personal well-being.

Interaction-Based Recommendations for Online Communities

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.

Smartphone Response System Using Twitter to Enable Effective Interaction and Improve Engagement in Large Classrooms

This paper proposes a method for seamless interaction between students and their professor using Twitter, one of the typical social network service (SNS) platforms, in large lectures. During the lecture, the professor poses surprise questions in the form of a quiz on an overhead screen at unexpected moments, and students submit their answers through a smartphone response system using Twitter. Since points are awarded on a first-come–first-served basis to only a limited number of the students who submit correct answers, students are required to concentrate on the lecture if they want to score. The proposed method solves both the problem of decreased concentration arising from frequent use of smartphones for nonlearning purposes in lectures and of inefficient interaction that occurs in large university classrooms. Evaluation through analysis of exam results and a post-course survey confirm that the proposed solution produces better results in terms of both students' academic achievement and their concentration during lectures.

Design of an Abstraction Layer for Communication with BOSH XMPP Server

XMPP (eXtensible Message and Presence Protocol) is a well structured and constantly evolving protocol that is being used for communication between a client and a server and from the server to one or a group of clients simultaneously. This protocol is being used in typical social networking applications such as Facebook, Google+, Google Hangouts, Twitter, etc. The client to server communication involves constructing long XML body for sending messages. Then the server adds more details in XML body and sends it to the appropriate client or a group of clients. At the receiving side parsing of XML body is more complex. At the client side, constructing the large XML body, which follows the protocol standards, is very difficult for a developer and blows up the development time. The developer not only has to know the protocol inside out but also has to write code that handles various cases. This calls for a simplified implementation of XMPP. The solution to this problem is to propose the design of an abstraction layer, which creates an object with which the developer interacts. The Object in turn handles the construction and parsing of the XML body. This object should also handle various connection issues gracefully and should be able to send the various presence stanzas, which the protocol requires. For using this object the developer imports the library and invokes the object and need not know any intricate details of the protocol. This idea of creating an object, which abstracts the XMP Protocol, is inspired by the MySQL JDBC (Java Database Connector), which is currently being used to connect to MySQL server. The proposed Abstraction Layer was developed using CoffeeScript and it was benchmarked using Chrome Developer Tools CPU Profiler.

Simulating Opinion Dynamics on Stakeholders’ Networks through Agent-based Modeling for Collective Transport Decisions

In transport planning several actors with conflicting objectives are involved in the decision-making process. Though public participation is fundamental to legitimate a transport plan, some inconsistencies may arise when individual preferences are aggregated into a collective decision. In this work, we reproduce the process of collective preference ranking among plan alternatives using agent-based simulations of the opinion dynamics on groups of stakeholders linked in typical social networks. The results show the efficacy of interaction and the relevance of the network topology to find a transitive and shared collective preference ranking.

개인화 영양정보 제공을 위한 소셜 네트워크 서비스 활용방안

소셜 네트워크 서비스 사용자의 개인 데이터를 활용하는 것은 개인화된 영양정보의 제공을 위한 새로운 자원으로서 활용 가능하다. 기존 영양정보 제공 방식과 비교하여 사용자가 입력한 소셜 네트워크 서비스의 기록을 바탕으로 맞춤화된 정보를 제공하는 방법 및 개인화된 영양정보를 어떻게 제공할 것인지에 대한 연구는 매우 부족한 실정이다. 본 연구는 대표적인 소셜 네트워크 서비스인 페이스북의 사용자들이 입력한 텍스트 데이터를 바탕으로 개인화된 영양정보를 제공하기 위한 방안을 확인하고자 하였다. 이를 위하여 사용자의 페이스북 게시정보를 분석하여 개인별 영양정보를 효과적으로 제공하는 방식을 제시하였다. 연구의 목적에 따라, 본 연구는 수집된 데이터를 이용하여 데이터마이닝 기법 중 군집화를 수행하였다. 사용자 데이터에 대한 군집분석 결과, 나트륨과 당류가 사용자의 식단에서 중요한 변수로 추출되었다. 추가적으로 판매원/제조원에 따라 사용자의 식단과 관련하여 변수의 중요도에 차이가 있음을 확인하였다. Personalized data of users at social network service can be used as a new resource for providing personalized nutrition information. Although providing personalized information for nutrition using social data, there are a few studies on providing personalized nutrition information with customized user preference based on social network service. The purpose of this study is to implement the clustering of data analysis with collected personal data of Facebook users. To find out the method for providing personalized information, this study described an effective method for providing nutrition information by analyzing web posting on Facebook that can be called a typical social network service. According to the result from clustering, sodium and sugars were important variables from diet of user. Furthermore, the importance of elements of user's diet has some differences according to vendor/manufactures.