Computational Social Networks最新文献

{"title":"Influence spreading model used to analyse social networks and detect sub-communities.","authors":"Vesa Kuikka","doi":"10.1186/s40649-018-0060-z","DOIUrl":"https://doi.org/10.1186/s40649-018-0060-z","url":null,"abstract":"<p><p>A dynamic influence spreading model is presented for computing network centrality and betweenness measures. Network topology, and possible directed connections and unequal weights of nodes and links, are essential features of the model. The same influence spreading model is used for community detection in social networks and for analysis of network structures. Weaker connections give rise to more sub-communities whereas stronger ties increase the cohesion of a community. The validity of the method is demonstrated with different social networks. Our model takes into account different paths between nodes in the network structure. The dependency of different paths having common links at the beginning of their paths makes the model more realistic compared to classical structural, simulation and random walk models. The influence of all nodes in a network has not been satisfactorily understood. Existing models may underestimate the spreading power of interconnected peripheral nodes as initiators of dynamic processes in social, biological and technical networks.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0060-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36824451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling the effect of religion on human empathy based on an adaptive temporal-causal network model.","authors":"Laila van Ments,&nbsp;Peter Roelofsma,&nbsp;Jan Treur","doi":"10.1186/s40649-017-0049-z","DOIUrl":"https://doi.org/10.1186/s40649-017-0049-z","url":null,"abstract":"<p><strong>Background: </strong>Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives.</p><p><strong>Methods: </strong>The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning.</p><p><strong>Results: </strong>By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today.</p><p><strong>Conclusions: </strong>The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-017-0049-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35764753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social learning for resilient data fusion against data falsification attacks.","authors":"Fernando Rosas,&nbsp;Kwang-Cheng Chen,&nbsp;Deniz Gündüz","doi":"10.1186/s40649-018-0057-7","DOIUrl":"https://doi.org/10.1186/s40649-018-0057-7","url":null,"abstract":"<p><strong>Background: </strong>Internet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to be exploited by smart attackers.</p><p><strong>Methods: </strong>To tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network.</p><p><strong>Results: </strong>We analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary.</p><p><strong>Conclusions: </strong>Our results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0057-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36714451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting random walk based sampling in networks: evasion of burn-in period and frequent regenerations.","authors":"Konstantin Avrachenkov,&nbsp;Vivek S Borkar,&nbsp;Arun Kadavankandy,&nbsp;Jithin K Sreedharan","doi":"10.1186/s40649-018-0051-0","DOIUrl":"https://doi.org/10.1186/s40649-018-0051-0","url":null,"abstract":"<p><strong>Background: </strong>In the framework of network sampling, random walk (RW) based estimation techniques provide many pragmatic solutions while uncovering the unknown network as little as possible. Despite several theoretical advances in this area, RW based sampling techniques usually make a strong assumption that the samples are in stationary regime, and hence are impelled to leave out the samples collected during the burn-in period.</p><p><strong>Methods: </strong>This work proposes two sampling schemes without burn-in time constraint to estimate the average of an arbitrary function defined on the network nodes, for example, the average age of users in a social network. The central idea of the algorithms lies in exploiting regeneration of RWs at revisits to an aggregated super-node or to a set of nodes, and in strategies to enhance the frequency of such regenerations either by contracting the graph or by making the hitting set larger. Our first algorithm, which is based on reinforcement learning (RL), uses stochastic approximation to derive an estimator. This method can be seen as intermediate between purely stochastic Markov chain Monte Carlo iterations and deterministic relative value iterations. The second algorithm, which we call the Ratio with Tours (RT)-estimator, is a modified form of respondent-driven sampling (RDS) that accommodates the idea of regeneration.</p><p><strong>Results: </strong>We study the methods via simulations on real networks. We observe that the trajectories of RL-estimator are much more stable than those of standard random walk based estimation procedures, and its error performance is comparable to that of respondent-driven sampling (RDS) which has a smaller asymptotic variance than many other estimators. Simulation studies also show that the mean squared error of RT-estimator decays much faster than that of RDS with time.</p><p><strong>Conclusion: </strong>The newly developed RW based estimators (RL- and RT-estimators) allow to avoid burn-in period, provide better control of stability along the sample path, and overall reduce the estimation time. Our estimators can be applied in social and complex networks.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0051-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35947442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus dynamics in online collaboration systems.","authors":"Ilire Hasani-Mavriqi, Dominik Kowald, Denis Helic, Elisabeth Lex","doi":"10.1186/s40649-018-0050-1","DOIUrl":"10.1186/s40649-018-0050-1","url":null,"abstract":"<p><strong>Background: </strong>In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics.</p><p><strong>Methods: </strong>For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web.</p><p><strong>Results: </strong>Our findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process.</p><p><strong>Conclusions: </strong>In summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35809194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Including traffic jam avoidance in an agent-based network model.","authors":"Christian Hofer,&nbsp;Georg Jäger,&nbsp;Manfred Füllsack","doi":"10.1186/s40649-018-0053-y","DOIUrl":"https://doi.org/10.1186/s40649-018-0053-y","url":null,"abstract":"<p><strong>Background: </strong>Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin-destination data and have difficulties when phenomena should be investigated that have an effect on the origin-destination matrix.</p><p><strong>Methods: </strong>A macroscopic traffic model is introduced that is novel in the sense that no origin-destination data are required as an input. This information is generated from mobility behavior data using a hybrid approach between agent-based modeling to find the origin and destination points of each vehicle and network techniques to find efficiently the routes most likely used to connect those points. The simulated road utilization and resulting congestion is compared to traffic data to quantitatively evaluate the results. Traffic jam avoidance behavior is included in the model in several variants, which are then all evaluated quantitatively.</p><p><strong>Results: </strong>The described model is applied to the City of Graz, a typical European city with about 320,000 inhabitants. Calculated results correspond well with reality.</p><p><strong>Conclusions: </strong>The introduced traffic model, which uses mobility data instead of origin-destination data as input, was successfully applied and offers unique advantages compared to traditional models: Mobility behavior data are valid for different systems, while origin-destination data are very specific to the region in question and more difficult to obtain. In addition, different scenarios (increased population, more use of public transport, etc.) can be evaluated and compared quickly.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":" ","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0053-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40538460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the role of participants in evolution of topic lifecycles on social networks.","authors":"Kuntal Dey,&nbsp;Saroj Kaushik,&nbsp;Kritika Garg,&nbsp;Ritvik Shrivastava","doi":"10.1186/s40649-018-0054-x","DOIUrl":"https://doi.org/10.1186/s40649-018-0054-x","url":null,"abstract":"<p><strong>Background: </strong>Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (\"bursty keywords\"), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques.</p><p><strong>Methods: </strong>In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a \"topic\"-a concept space-that are used by a large number of tweets.</p><p><strong>Results: </strong>We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter.</p><p><strong>Conclusions: </strong>We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0054-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36432600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contextual polarity and influence mining in online social networks","authors":"Hassan Alzahrani, P. Duverger, Nam P. Nguyen","doi":"10.1109/DASC-PICom-DataCom-CyberSciTec.2017.172","DOIUrl":"https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.172","url":null,"abstract":"Crowdsourcing is an emerging tool for collaboration and innovation platforms. Recently, crowdsourcing platforms have become a vital tool for firms to generate new ideas, especially large firms such as Dell, Microsoft, and Starbucks, Crowdsourcing provides firms with multiple advantages, notably, rapid solutions, cost savings, and a variety of novel ideas that represent the diversity inherent within a crowd. The literature on crowdsourcing is limited to empirical evidence of the advantage of crowdsourcing for businesses as an innovation strategy. In this study, Starbucks’ crowdsourcing platform, Ideas Starbucks, is examined, with three objectives: first, to determine crowdsourcing participants’ perception of the company by crowdsourcing participants when generating ideas on the platform. The second objective is to map users into a community structure to identify those more likely to produce ideas; the most promising users are grouped into the communities more likely to generate the best ideas. The third is to study the relationship between the users’ ideas’ sentiment scores and the frequency of discussions among crowdsourcing users. The results indicate that sentiment and emotion scores can be used to visualize the social interaction narrative over time. They also suggest that the fast greedy algorithm is the one best suited for community structure with a modularity on agreeable ideas of 0.53 and 8 significant communities using sentiment scores as edge weights. For disagreeable ideas, the modularity is 0.47 with 8 significant communities without edge weights. There is also a statistically significant quadratic relationship between the sentiments scores and the number of conversations between users.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"8 1","pages":"1-27"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48132306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling and analysis of the dynamics of adaptive temporal-causal network models for evolving social interactions.","authors":"Jan Treur","doi":"10.1186/s40649-017-0039-1","DOIUrl":"https://doi.org/10.1186/s40649-017-0039-1","url":null,"abstract":"<p><strong>Background: </strong>Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.</p><p><strong>Methods: </strong>Adaptive temporal-causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they themselves also change over time.</p><p><strong>Results: </strong>It is discussed how modelling and analysis of the dynamics of the behaviour of these adaptive network models can be performed. The approach is illustrated for adaptive network models describing social interaction.</p><p><strong>Conclusions: </strong>In particular, the homophily principle and the 'more becomes more' principles for social interactions are addressed. It is shown how the chosen Network-Oriented Modelling method provides a basis to model and analyse these social phenomena.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"4 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-017-0039-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35678201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method for evaluating discoverability and navigability of recommendation algorithms.","authors":"Daniel Lamprecht,&nbsp;Markus Strohmaier,&nbsp;Denis Helic","doi":"10.1186/s40649-017-0045-3","DOIUrl":"https://doi.org/10.1186/s40649-017-0045-3","url":null,"abstract":"<p><p>Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the discoverability and navigability of recommendation algorithms. The proposed method tackles this by means of first evaluating the discoverability of recommendation algorithms by investigating structural properties of the resulting recommender systems in terms of bow tie structure, and path lengths. Second, the method evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three data sets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis, and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"4 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-017-0045-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35677781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}